In our first interview for the podcast, we talked with Dr. Seyed Kazempour about his paper called Finfluencers. It's a fascinating paper based on research on StockTwits. The conclusion? The more followers someone has on StockTwits, the worse their market predictions.
We discuss the implications of this research for traders.
Here's a link to the Finfluencers paper.
Here's a link to Dr. Seyed Kazempour's website.
We discuss the implications of this research for traders.
Here's a link to the Finfluencers paper.
Here's a link to Dr. Seyed Kazempour's website.
Creators & Guests
What is Line Your Own Pockets?
A weekly show for systematic traders who want to make more money from their trading strategies.
Okay everyone, welcome back to Line Your Own Pockets We have a very interesting episode today where we actually are chatting with Saeed who is gonna tell us a little bit about some quantitative aspects from the social media space and and how that can kind of affect trading from a retail side of things, how we can kinda keep an eye on what's going on in social media and potentially generate some alpha in a systematic and quantitative way. First of all, thanks for joining us. Thanks for coming by and and chatting with us. It's awesome to find, fellow kind of quantitative systematic style market participants. Can you tell the audience a little bit about yourself before we get started?
Seyed Kazempour:Yeah. Thanks very much for having me. I'm doctor Syed Kazimpour, assistant professor of finance at Louisiana State University. And, well, I've been working on this project that, you guys, well, we are going to discuss today for about a couple of years. And, most of my work has been, on how people communicate, in financial markets.
Seyed Kazempour:And part of this communication obviously happens in social media where people, talk about their investment ideas, whether good investment ideas or, you know, less worthy ones. So and that's how that that work come to be. I've got the other pieces of work too, but mostly, I focus on how people communicate and talk to each other.
Dave:Excellent. So, yeah, again, thanks for being here. Just a little background about how I came to know about this paper that you wrote. A friend of mine sent me the paper, I think, in May or so, And I read it, and the headline's pretty amazing. Right?
Dave:It's a pretty cool headline. The headline is basically, the more social media followers you have, the more antiskilled you are. So the, like, the more money you could make literally doing the opposite of what somebody does, which is, like, such a great thing. I just I knew it would resonate with a lot of our listeners. I so I I worked that into a post to my newsletter, and I sent it out to the newsletter, and I could tell it resonated big time.
Dave:Like, I got some responses. People like, wow. This is really cool. And the more I thought about it this is back in, I think, June. And the more I thought about it, the more we've been doing this podcast.
Dave:I thought, man, this would be a really cool topic because I think our listeners are, you know, they're sophisticated traders. They're they are they're over the hump where they they realize that to be successful, they have to really take control of their own trading. They don't need to be following other people. So I think this is an intuitive conclusion for them. So I knew I'm just excited about this conversation.
Dave:So tell us how you originally got involved with the idea about this paper, and how did it come together with your collaborators?
Seyed Kazempour:Well, I I had been working on social media, for another project, a project with surprisingly different result, because it focuses on a different, set of demographics. But, anyway, I've been working on that project, and, the idea of separating good from bad or skilled from not so skilled or antiskilled, whatever you wanna call it, have been around has been around that idea, in but in other contexts, in, let's say, among analysts, among mutual fund managers, so on and so forth. And then we had this idea of why don't we just apply to all the advice on social media? And I discussed that idea with a friend of mine, one of the people on the on that paper, and then, we just found people who were also interested in this question. And that that's how that team came to be.
Seyed Kazempour:And, from there, it was it was relatively smooth sailing, really. You get the data. You work on it. You apply the methodology, and, it turns out that we've got some interesting results.
Michael:Absolutely. So I'm just gonna quickly summarize. Again, we will be putting the paper in the descriptions and and everywhere that you're watching or listening to this so you can find it. But let's just summarize, basically, the the the top line's idea behind it. And the idea was that you wanted to kind of take a look at people on social media, the influencers, which, you know, I I love the name.
Michael:I think it's, it's everyone hates influencers, so just adding the finance to that, I think, actually works pretty well. But the idea was, is there any advantage to following a lot of these big names that you see out there? Is there any money to be made above and beyond the overall market? Again, for all the details, definitely recommend you read the paper, but, essentially, you ended up breaking people into 3 groups. There was your skilled, your unskilled, and your antiskilled.
Michael:And the the idea is that these are the the skilled or the people that are doing well and have some kind of alpha outside the market. Your unskilled was just random luck. Sometimes great. Sometimes, you know, whatever. They're they're and then the anti skilled were these people were actively bad.
Michael:So they were everything that essentially they put out or a lot of things, majority of things, could be taken as the other side of that. Now I don't think that it surprises or will surprise a lot of our audience that those groups exist that, you know, there's some people out there that have some edge over the market. There's some people out there who are just, you know, tweeting, and and they end up probably getting market returns and just doing a whole lot of work. And then there's some people who are bad. I guess one of my question is, were you shocked at the end on that distribution that it's very, very weighted to people?
Michael:The larger you are, the worse you are. And it's not even like a it's not even a little bit. I think that was the big thing when I was reading the paper. Is it's not like you found this kind of slight edge that, you know, some people on social media aren't very good at markets, which I think we all know, but it's, like, the vast the the vastness of the people who are bad at markets. Is that was that shocking, or did you kind of expect that going in?
Seyed Kazempour:It was, it was a surprising result in the sense that that's, to the best of my knowledge, the only setting where we see so many bad so much bad advice. And so the distribution was certainly a new aspect of the finding. That was novel. How much did we actually expect social media influencers to be skilled and have positive alpha? Well, different people have different expectations, really.
Seyed Kazempour:I can tell you, I wasn't super surprised when I found that most of the advice, wasn't wasn't really going anywhere. But I when talking to colleagues, I have met people who who have different priors. They thought, well, if these people are giving advice and people are following it, there should be at least some wisdom, or some wisdom of the crowd in terms of following. People who are good should ultimately get more famous and have more followers. It turns out that the exact opposite is observed in the data.
Dave:Yeah. That's so interesting to me to you know, you hear this wisdom of the crowds, and that in certain context, that's very much true, very much verifiable. But yet in this context, exactly the opposite is true. Why do you think in this context that's the case? Why wouldn't the wisdom of the crowds be, you know, prevalent and, you know, verifiable everywhere?
Dave:Why is it why is this one unique, do you think?
Seyed Kazempour:In the context of financial markets, we have known biases. We we tend to, for example, chase returns. If a stock has been go doing doing well, everybody seems to clamor on. They they buy into it. We have certain known patterns that aren't too productive, and those lead us to to to bad decisions in financial markets.
Seyed Kazempour:And you have to realize, financial markets are extremely competitive. They are probably the most efficient markets we have created.
Michael:Mhmm.
Seyed Kazempour:Most markets are not that efficient because it it's trading is happening every second in financial markets. News is coming out every second. There is all this machinery built in to make the financial markets more and more efficient. Now when you talk about whether we can produce alpha or not, you're really thinking, can we beat this market? Can we beat this super efficient market?
Seyed Kazempour:And it seems with the efficiency of the financial markets that we are at a level that those biases that we have, that we tend to chase returns, for example, that we we tend to buy into stocks that are in the news, and we forget that everybody sees the news, and everybody sees how this stock has been doing, so on and so forth, those biases start to hurt us. Whereas in in in less efficient settings, even if we have a little bit of a bias, overall, we're gonna get a decent result. But if you're talking about, can we beat this super efficient market? Well, you have to be super efficient yourself, and that doesn't seem to be happening. But by following, FEMfluencers who also have the same kind of biases that you do.
Seyed Kazempour:So not only are they producing bad advice because they have their own biases, but those biases appeal to yours. You you like people with the same biases, people with the same opinions. You think they must be right because they agree with you. And so you tend to follow people with the same biases. And that really doesn't work well in the financial markets.
Michael:So yeah.
Dave:So you mentioned the word, in the paper a couple times, homophily, which, I think, describes what you were just talking about. Could you define that and go into a little bit more about why that's an important topic with, what we're discussing?
Seyed Kazempour:Yeah. Homophily, in the paper, refers to exactly what I just described, that we have certain biases, we think in a certain way, and we tend to like people who think like us. We tend to like people who have the same kind of biases, the same kind of beliefs. Even if we are wrong, we tend to like those positions more than than the opposite. Homophily obviously means liking someone like yourself, similar to yourself.
Seyed Kazempour:And we are using it in the in the sense that well, in not a very good sense, in the sense that, you know, you you like people with the kind of opinions that ultimately will hurt you.
Michael:So I I think this is a really interesting road to continue down. My questions are more on, intent. Like, is the intent of the individual to grow a large audience, and therefore, they're intentionally putting out opinions they know will, will be favored, or is it just that the largest people kind of naturally get the the biggest? I don't know if you've heard of a term called audience capture. It's a fairly new one.
Michael:I know streamers use it all the time. But, essentially, it just means that, for example, if I'm a a a left wing pundit and I come every day and I I I wanna support my political party, whatever that is, I am very motivated to kind of ignore all the good or ignore all the bad things that that party or whatever the sports team or put in whatever, do and then highlight all the good things. And it is not so much that, right, I'm intentionally lying or misleading, but I know that if I say x, y, z thing, then a lot of my audience is gonna be upset with me. So do you think it has to do a lot with the fact that is are people going into this when they're making these tweets and they're making these recommendations saying, I'm gonna put this out there because I know it's going to appease my audience and potentially grow it? Or do you think it's just that it's kind of natural that people are are just saying the things that they believe and then people are are kind of tagging on to that?
Seyed Kazempour:The intent is a little bit difficult. I'm I'm hesitating to exactly ascribe, I guess, malintent to bad or to, you know, the type of tweets that, ultimately end up with negative alphas. I I don't know how much malintent there is out there. It wouldn't be the again, people have different priors about it too. I would I wouldn't think there is too many people who are just trying to mislead people intentionally.
Seyed Kazempour:My prior would be that most people are just posting to it because that's what that's what they believe in, but their beliefs are just wrong.
Michael:Well, that's interesting because yeah.
Seyed Kazempour:I can't deny that.
Michael:Oh, sorry.
Seyed Kazempour:Go ahead. I can't deny that this type of, setup might provide an environment where some people might misuse it. I mean, you can pump and dump in any kind of, or, I guess, misleading misleading audience, can have, can happen in multiple environments, right, not just on social media. I wouldn't be surprised if tomorrow or or if it turns out that some people use, social media to mislead others. I wouldn't just rule it out, but I wouldn't I would be surprised if that were the majority.
Michael:Yeah. Because I'm I'm always just very interested in the in the psychology of it and and how, that's able to persist the, the ability to, again, just be wrong so often. It's one of those where if you go in to a dentist and he pulls out the wrong tooth even once, right, that guy is gonna have a hard time. But with, I don't know if it's the randomness of the markets or the randomness of returns or what it is that allows people, to not only grow, but to then to sustain that audience. If the person is putting out information that is so incorrect so often, you would imagine there's kind of a Darwinism type effect that would occur at some point where these people would just end up leaving that person, en masse and and going to somebody, and that, you know, you wanna kind of think as, you know, someone who's a capitalist and and looking at all these things that there would be these self correcting mechanisms in social media.
Michael:So I think to me that was one of the most interesting parts is that it it there doesn't seem to be a rebalancing effect their way it would be in any other market out there when it comes to, you know, if I'm going to the grocery store and I'm I'm constantly getting charged more than this other place, I just move. And then, right, things rebalanced. So, I just wonder kind of your insights on that. Is there do you think there will come a point where people start to catch on and and people rebalance and move? Or is it just part of the the randomness and the chaos and the emotions of the market are gonna just keep everything moving in this direction?
Seyed Kazempour:Oh, that's a you are asking, one difficult question after another.
Michael:I'm sorry. I I just I got really interested by this, so, of course, my brain just
Seyed Kazempour:started to We we can't we can't talk about it. These are definitely important questions. From from an academic perspective, it's sometimes hard to answer these questions because you you need just you need, I guess, proof, and it's hard to prove these things, especially when it comes to predictions of future. It's certainly difficult to distinguish between people who are skilled and who can provide good advice and the people who can't exactly because of the randomness in returns. So I would I wouldn't be surprised if there were a mechanism where these people could persist in the market.
Seyed Kazempour:But then again, people also the the people who listen to, or tune into, I guess, this kind of advice also have the opportunity to unfollow them. But then they might be replaced by new people who find them online and keep and start following them. So it's really hard to pin down these dynamics. I can't really make a prediction as to whether whether this is gonna persist in the future or not. Yeah.
Dave:So one question I have. So so you this all came from Stop Twits. Correct? So there wasn't any Twitter data included here. Was it hard to get access to the API and to access all this data?
Dave:Did you have to get permission, or is this publicly available and you just, went after it and gathered the the the data that you needed?
Seyed Kazempour:Well, I have to thank my co authors for collecting the data on that. Stocktits and Twitter both are pretty popular, for people who talk about stocks on social media. They have a feature, a cash tag feature, where you can add the ticker of a stock after a dollar sign. And then it's kinda like a hashtag where you're you're pinning down that this tweet is about this kind of a stock, or you're you're exactly mentioning that stock. That's one of the reasons these 2 are quite popular.
Seyed Kazempour:There are other platforms as well. For us, accessing the data on StockTwits was easier. And I know there are people who there are other people, other colleagues who have also access to that data and the data on Twitter and other social media. We should also say that when we say FIMfluencers, we have looked at this specific social media. There are other kinds of platforms that, to the to the extent that we can tell, seem to have better content in terms of, you know, more, skilled, I guess, influence or more skilled, people, analysts, whatever you wanna call them, on those platforms.
Seyed Kazempour:So there there does seem to be an effect from that platform. Some platforms are better than others in terms of the, average value of a skill in them.
Dave:Yeah. One thing that stuck out at me in your paper was the discussion on Seeking Alpha and how that had sort of the opposite, correlation. Can you talk a little bit about how that platform differed from Twitter? I mean, obviously or or Stocktwits?
Seyed Kazempour:Yeah. I should first mention that that's that's not our work, so we should we should give credit to the author of of that paper. The Seeking Alpha is inherently a different kind of platform in that people post analysis rather than just a short message. And, they they have an editorial process as well. And to the to the best of my knowledge, the, people, the analysts who post their analysis on Seeking Alpha, are also paid.
Seyed Kazempour:At least there is a mechanism for paying certain analysts or or a subset of the analysts in Seeking Alpha, at least. So there are some inherent differences both in the terms of what you post on these platforms on Seeking Alpha versus Stockbridge, how much you can write, what a typical analyst writes or a typical influencer writes on those platforms. These are there there are significant differences between the these platforms. I wouldn't be surprised if those differences ultimately played a role in who goes where to post this analysis.
Michael:Well, that makes a lot of sense. You get, you know, people just blurting out opinions on stock twits as they come, but, yeah, a more editorialized longer form content. There's at least more thought that goes into the the ideas that that that's kind of put out. So, to me, that makes a lot of sense. Now, you know, this this paper has been out for a little bit now.
Michael:Have you been contacted by anyone for an attempt to actually trade this? Like, because, right, we're we're traders and investors, me and me and Dave. So, of course, when I'm reading this, I'm thinking in my head about a way to potentially, you know, you found a a fairly large edge. So the the the issue is have either yourselves or have you heard of anyone actually successfully exploiting this edge? Or you don't need to give us the the nitty gritty, but, I would imagine it would be very, very complicated to do.
Michael:But, it does sound interesting, and and it actually does sound kind of fun to say, hey. You know, I'm doing the opposite of what what these big names are, and and this is the the profits that I'm making above and beyond the market.
Seyed Kazempour:Well, there are firms that use social media as a signal in their trading strategy. How they use it, I'm not aware. I I imagine they wouldn't exactly outright tell me. But, so I don't know if they're they're distinguishing between, you know, skilled people on social media and unskilled or antiskilled people. We're using social media as a signal in trading, though that has been around for a while.
Seyed Kazempour:Nobody has contacted me for for advice, and I I kind of I'm thankful for that because I don't give advice, really. I should also say that nothing I say today is strictly is is financial advice, really. I'm just, here to share what I have found in my research. Yeah. So social media has been around, and using social media as part of a trading strategy also has been around.
Dave:So here's a question about the skilled, unskilled, and antiskilled. Let's say let let's say I started posting tweets during this time period from which you have data, and the only thing the tweet said was buy the spy. Every day, I said buy the spy. So that would be, you know, exactly in line with the market. What how would that user be categorized?
Dave:How would that user be categorized in your study here? Would that be skilled, unskilled, or antiskilled?
Seyed Kazempour:A user that just talks about the market would not be would not be in our data at all. You have to talk about the specific stocks, to be in our data. So it's actually that's actually a very good point. Not all advice is included in our sample. There are technical, issues.
Seyed Kazempour:So for example, there there are a lot of people talking on social media about how to make money, generally, how to make money. That's not the kind of person we have in our data. You have to talk about a specific ticker, and that ticker has to belong to a specific traded stock, for us to be able to measure alpha. If you were constantly saying buy S and P 500 or buy a spy, you would have an alpha of exactly 0, because all your earlier sayings get the market. Yeah.
Seyed Kazempour:You would systematically have an alpha of precisely 0. So, even if we we did include them, which we don't, if even if we did include them, that would be alpha equals 0.
Michael:Which is hilarious. I was just gonna say hilarious because according to the data, you're beating most the large majority of all all influencers out there. So if you wanna if anyone's, you know, listening to this and you wanna say, hey. I'm better than most social media influencers. Just go out every day and tell people to buy the S and P 500, and you now have data that you're better than the large chunk of them.
Seyed Kazempour:Or or don't, really, or say nothing and just claim that you're better than most
Michael:of them.
Dave:Right. So, one quote that, stuck out of me in the paper here, still correlates with observable tweeting patterns.
Seyed Kazempour:Yeah.
Dave:Can you go into the details behind that and what you discovered there about the, you know, more tweeting and the negative tweets versus positive tweets and what that meant whether you were skilled or unskilled or or antiskilled?
Seyed Kazempour:Yeah. We find out and that's very interesting. We find there there are certain tweeting patterns that we know are associated with positive or negative skill, or at least we have some evidence to say they're associated with positive or negative skill. We know that people who, for example, chase returns, who just talk about stocks that have been going up over the past months or past week or whatever, Those stocks tend to go down. On average, they go down.
Seyed Kazempour:So if all you're doing is talking about stocks that have been going up over the last month, on average, your your average alpha is gonna be negative, and that's observable. You may not know somebody's alpha just by, looking at their Twitter, but if you notice the patterns of their tweets or stock tweets, excuse me, if you just notice the patterns that they use in their tweets, that might be a very different story. You can tell that this guy is only tweeting about stocks that have been going up and and tweeting positively about them, or he's tweeting negatively about stocks that have been going down. We do know that these patterns tend tend to reverse, and so if somebody is just doing that, they should have a negative alpha. So it's although skill is in and of itself largely unobservable, but certain patterns, certain observable patterns tend to correlate with skill.
Seyed Kazempour:And but but we find out that for the most part, that doesn't show up in the followers. The people or the number of followers tends to either not correlate with those observable patterns or correlate in the wrong direction. And so there are these observable and there are more, not just I my example was chasing returns. But, for example, we do know that which it's much easier to be bullish on everything than to have a negative opinion about us or about a significant number of stocks and, and and stick to those opinions. And so in the market, for example, short sellers are are more known for being skilled.
Seyed Kazempour:Right? It's it's harder to be a short seller. So we do have this idea that negative, opinions or or or, you know, negative, or, I guess, negative beliefs about the future of a stock. That tends to more correlate with the skill, and we do find that in our data too. We find that if you look at the people who post mostly negative tweets, right, They tend to be more skilled.
Seyed Kazempour:They are they are more likely to be skilled. Or for example, you can't have a whole lot of information about a whole lot of stocks. If you're tweeting in large volume, then it's less likely that you're gonna be super skilled with every one of your tweets. So these are patterns that, again, none none of these correlations are 100% correct. So but, overall, they should give you a sense of who is more likely to be skilled and who is more likely or less likely to be skilled.
Seyed Kazempour:And we find that most people or or at least when looking at the number of followers, that doesn't seem to hold in the data in the sense that peep yes. The the patterns predict skill, but the same patterns don't predict followership or predict it in the wrong direction.
Michael:So that's that's interesting. So just a quick question on the stock selection. Did you look at at all different different attributes of securities being tweeted about? Like, were, you know, people exceptionally worse at penny stocks as opposed to, you know, larger cap, more profitable names or was just kind of a stock looked at as a stock? Because, you know, it's one of those things that when I think about you saying that, you know, the short sellers end up being more right, that just kind of makes me think that most junky stocks that are that are showing positive return will revert and and kind of crash back down.
Michael:But I you know, thinking of something like, an Apple or an Nvidia or Google or something like that, we're trying to be negative on something like that. That's a more profitable name over the long run, that would be a lot harder game. So was there any differentiation between different market caps or profitability or anything like that?
Seyed Kazempour:So there there are a number of stock characteristics, and some of some of them have been associated with, you know, higher returns or higher alpha and some of them with lower alpha. We we looked at some of those, but not all. And, so if you think about so for example, one of the other things that we looked at was what the short selling rate is or, the variable that we looked at was, short selling constraints, how hard it is to short sell a stock.
Michael:Mhmm.
Seyed Kazempour:Now if it's very hard to short a stock, we know from, finance theory that that stock is more likely to be overpriced because it's just hard for negative information to get incorporated in prices. And so I think a lot of, a lot of what you mentioned, can be incorporated into, for example, short selling constraints. How hard is it to go against Apple, you know, or or short the Apple, the stock of Apple? That that that is correlated with the short selling constraints. And we do find the same pattern in there as well in the sense that, if a stock is more is harder to short sell, it's more likely to be overpriced.
Seyed Kazempour:And so if you're positive if you're tweeting about it positively, you're more likely to be wrong, and that's relatively predictable. But, again, that's not something that affects the followership.
Dave:So one thing that, I was thinking about is this as I was reading this, The findings do seem pretty consistent, but how is the magnitude of the effect how do you measure that, and and how can you compare that against other findings in the field? Can you give us a sense of how big the magnitude is here?
Seyed Kazempour:You mean the magnitude of, of what? Of alphas or or of skills, I guess?
Dave:Yeah. I'm I guess I'm just thinking, you know, how does this compare to other findings in the field as far as the magnitude? Like, is this is this consistent but tiny? Is it consistent but big compared to other common findings or things in the literature?
Seyed Kazempour:I think the I might get the numbers a little bit wrong, but, the average skill level for for an anti skill is between 1 2%, and that's per month. And so the relative the magnitude is relatively big. And it's, so but it was smaller on the skilled side. So, compared to other I mean, if it depends what your benchmark is really. I would I would call this a relatively big magnitude of negative skill, actually.
Seyed Kazempour:I mean, 1% per month is, is pretty large. If you use what lose 1% per month, it won't be long before, you have to get out of the market. True. So I would I would call it a relatively large magnitude, actually.
Michael:Mhmm. So I I think some of the some of the takeaways for for our our audience of investment traders, should be fairly obvious right now being that, you know, it makes way more sense to do your own work and to, you know, understand your own process and and investment thesis or or trading plan than it is to is to follow others. But on top of that, can you think of anything that, you know, there there's someone out there who's a retail trader and investor, and they're they're reading through this process and and they're reading through this paper and and they're kind of assimilating all the information. Is there any other kind of, hey. You know, you should notice this thing that I noticed as opposed to probably don't listen to what you hear on the Internet, which I think is probably just a good advice for everyone in this day and age.
Seyed Kazempour:Yeah. Well, reiterating that nothing I say is financial advice. On top of generally not trusting anything you hear on the Internet, I would say the more information you you have about that person, I guess more inform you should you should, or I I shouldn't say you should. You should definitely not trust someone you know nothing about. That's for sure.
Seyed Kazempour:Mhmm. So maybe that's one one good way of putting it. Like I said, there are, other platforms where you just get more information about whatever it is that that person is, providing. So on Seeking Alpha, for example, you have a long, longer analysis of of that particular stock. That longer analysis allows you to get into why that person thinks this stock is overpriced or underpriced.
Seyed Kazempour:And so that's an extra information that should be better than just a short tweet by such and such. That that's one thing. I can also talk about a little bit about another piece of work that I have, which focuses on registered financial advisory firms. And these are firms that have been registered with the SEC. The their job is to provide financial advice, and some of these are on Twitter.
Seyed Kazempour:They have Twitter accounts. And it turns out that some of them also post about specific stocks or or post information even if not advice. I mean, it's it's some of the things that they post aren't strictly advice. They just post information.
Michael:Mhmm.
Seyed Kazempour:And my other piece of work shows that, they actually tend to be relatively good. Their average alpha is positive. And so, on the one hand, you have more longer analysis, more information, more background story of what that advice is helps. On the other hand, if you've got people who are regulated, who who you know, these guys are registered with the SEC. It's their job to provide advice.
Seyed Kazempour:These guys seem to be doing better than the the average influencer, at least to the to the extent that I can tell. Even on the same social media or on similar platforms.
Michael:And that makes sense. More to more to lose, I guess, for them as well. Right? They they can't, yeah, they can't speak out of turn. They have to have some sort of research behind what they're doing.
Michael:And and if they're consistently putting out bad advice, then it probably also means their clients are consistently not doing well. So I guess there's a more to lose aspect there as well as opposed to just, you know, writing off a sending off a tweet.
Seyed Kazempour:Yeah. That's part of it, and that's part of, why they're professionals. Right? So that's what you would expect. Yeah.
Seyed Kazempour:I think do your own research. I guess that would be number 1.
Dave:Of course.
Seyed Kazempour:But and not trusting people you don't know is probably a good advice too. I mean, I don't know. I I find, my research shows that, you you can't really trust a lot of the things you hear on social media. And the more information you can get, that should be better.
Dave:Mhmm. So I'm curious. Has anybody from Twitter or reached out to you, like, nitpicking about something in your findings or sort of taking issue with it in any way?
Seyed Kazempour:No. Nobody has reached out to me, and I don't think anybody has reached out to my coauthors either. And, no, they they haven't they haven't reached out to Nitpick either. So, they have been, I guess, pretty lenient in that sense. Or I guess maybe lenient is not the right word.
Seyed Kazempour:Yeah. They they haven't been putting any pressure on us.
Dave:Yeah. That's interesting. We, Michael and I both know people that started StockTwit, so I'm curious. The the silence is curious, and I'm, I I think I'll ask, my buddy about what he thinks about this or if he's heard about.
Michael:Uh-oh, Dave. We lost you a second. You're back now. That's fine. Did you know I don't know if you noticed this, Dave, but recently, they actually launched a sentiment tool inside Stocktwits per security.
Michael:So if you go look at a security, there's just like a little a little gauge that will show you. So I when you're asking them, ask if there is some sort of influence or or motivation to adding a a readable sentiment score for every single security based off of of what you saw because, I admit I hadn't been on Stockpits for years. But then after reading this, I went back to to check it out, and I noticed this. And and it just made me think, were they you know, is there is there some correlation here? Did someone read the study and say, oh, well, we actually have, some valuable data if if you know how to use it.
Michael:So now let's talk about, you know, this this sentiment data score, and and I think probably most people are using it the opposite. You know, they're looking to buy things with good sentiment and sell things with bad, but it it just makes you think that there there could be now that this tool exists, there could be some, practical applications for it.
Dave:Yeah. So, Syed, what, do you have more plans for the future for this research? What are you excited about working on, as far as related to this or any other work that you might be doing?
Seyed Kazempour:As I said, most of my work is focused on how people talk. We are pretty excited about this paper. We hope that it will get published pretty soon. And, I have other pieces of work that use similar data. Like I said, I've got another work that, focuses on registered financial advisory firms with kind of the opposite result, really.
Seyed Kazempour:And so I'm pretty excited about that, and I will definitely keep working within the same area in in the future. So we'll see what the future holds for us, I guess.
Dave:Well, that's That's great. Well, if anybody listening wants to get in touch with you, to comment on this or get in touch with you anyway, how would they do that? What's the best way to get in touch with you?
Seyed Kazempour:My website, is, online at smkazempore.com, and, you can find my contact information on there, including my email address and, my LinkedIn.
Michael:I I was gonna ask how many followers you had because we know if it's a small amount, then we should probably listen more than than a large amount. Right? That was the takeaway.
Seyed Kazempour:Well, it's LinkedIn, so, I don't know about that. I can tell you I don't have a whole lot of followers on Twitter. So Okay. It's, again, not not financial advice.
Michael:I'm gonna go follow right now, and I'm just gonna Yeah. Absolutely. The house into whatever. But, yes, thank you very much for coming by. We appreciate it.
Michael:We'll follow whatever you put out next, and, you know, if you're interested in coming on and talking about it, that's great, and and we'll certainly be in touch. Had a had a lot of fun. Thank you again.
Seyed Kazempour:Thanks thanks very much for having me. I had
Dave:a lot of fun too.
Michael:Awesome. Have a good one, guys. You
Seyed Kazempour:too. Bye.