Traders trade information or they don’t
We investigate whether social trading platforms facilitate information sharing networks by analyzing 2.2 million trades executed by 11,295 traders during 2023-2024. Using network analysis methods, we identify clusters of traders who coordinate on illiquid, low-attention stocks ahead of substantial price movements, achieving exceptional returns (50-100%+) that substantially exceed market benchmarks. These patterns persist across both years with consistent network structures despite involving different stocks and traders. While we cannot establish the information sources or communication channels, the combination of stock selection, entry timing, and exceptional returns is difficult to reconcile with public information trading, technical analysis, or influencer following.
Introduction
Online social trading platforms have created an ecosystem where individual traders publicly share their investment strategies and portfolio positions. Some participants operate as fund managers, accepting retail investor capital in exchange for management and performance fees. Others build personal brands, monetizing through subscriptions and affiliate marketing. Still others maintain public trading profiles without direct monetization. What these arrangements have in common is that there’s an online public record of trades.
From a standard economic perspective, these retail-focused strategies should underperform market indices. The Efficient Market Hypothesis [1] suggests that publicly available information is already incorporated into prices, leaving no exploitable inefficiencies for retail traders. Investors in these strategies would be expected to receive market-level returns with higher volatility and reduced net returns after fees — hardly an attractive proposition. And yet, when you look at these platforms, certain traders do seem to outperform the market consistently.
| Annualized Volatility | |
|---|---|
| Trading Platform | 53% |
| S&P 500 | 18% |
| EuroStoxx 50 | 16% |
| Correlation | |
|---|---|
| S&P 500 | 17% |
| EuroStoxx 50 | 10% |
The obvious explanation is survivorship bias: successful traders remain visible while unsuccessful ones drop out from the platform. However, examination of user identification numbers reveals no systematic account deletion — IDs are assigned sequentially without large gaps that would indicate systematic removal of unsuccessful accounts. Additionally, many outperforming traders select low-attention, thinly-traded stocks that don’t fit typical retail preferences, which undermines explanations based purely on luck in picking popular growth stocks like Apple or Google.
This raises an interesting question: could these platforms facilitate the monetization of privileged information? We hypothesize that they may enable an information trading economy [2], where:
- Traders receive privileged information from informants rather than possessing direct access themselves.
- Informants share information with multiple traders rather than single recipients.
- Traders further disseminate information among their networks, since information can be duplicated at zero marginal cost.
This study explores this hypothesis through quantitative analysis of trading network data. We examine whether clustering patterns and correlated trading activity among platform participants can be systematically identified, and whether such patterns predict subsequent price movements.
This study is conducted solely for personal purposes; it does not identify specific platforms or traders and makes no allegations of illegal activity against any individuals or entities.
Methods
Data
Our dataset consists of 2.2 million stock trades executed by 11,295 traders on an online social trading platform from January 1, 2023 to December 31, 2024. Daily closing prices for all ISINs were independently sourced from Yahoo Finance, where available. For each trade, the dataset contains the timestamp of the trade, an anonymized trader ID, the ISIN of the underlying stock, the stock price at which the trade was executed and the direction of the trade - buy or sell.
The dataset does not contain the size of the trades (i.e., the number of shares bought or sold) or any information linking specific buy and sell transactions. While we observe individual buy and sell trades with their timestamps and prices, we cannot determine which prior buy trade a trader is closing when they execute a sell trade. The platform does not allow short selling; therefore, all sell trades represent the closing of previous long positions, though the specific pairing cannot be identified from the available data. For our analysis, we aggregate trades at the monthly level by trader-stock pairs, calculating net positions to determine directional exposure rather than tracking individual trade-level returns.
Coordination Detection Methodology
We identify potential insider trading networks by analyzing coordinated trading patterns and their subsequent profitability. Our methodology consists of the following steps:
1. Monthly Trade Aggregation: For each calendar month from January 2023 to December 2024, we aggregate all trades by trader-stock pairs, calculating net positions (buy trades minus sell trades) to determine trading direction (long or short). We exclude neutral positions where net trades equal zero. For each trader-stock-month combination, we record:
- Trade count: the number of trades executed.
- Direction: long (net positive position) or short (net negative position).
- Total trading activity: the trader’s total number of trades and unique stocks traded that month.
For each stock during each month, we calculate:
- Stock popularity: the number of distinct traders who traded the stock.
- Stock volume: the total number of trades executed on the stock.
- Forward return: the percentage change in stock price over the subsequent month.
2. Coordination Identification: A trader pair is classified as coordinated when both traders take the same directional position (both long or both short) on the same stock during the same month. We generate all possible trader pairs who traded the same stock in the same month, creating a comprehensive record of potential coordination instances.
3. Profitability Classification: To assess whether coordinated trades preceded price movements, we classify each coordination instance as profitable or unprofitable. A coordinated trade is classified as profitable when the one-month forward return aligns with the trade direction: positive returns for long positions or negative returns for short positions. This forward-looking profitability metric captures whether the coordinated trading behavior predicted subsequent price movements.
4. Filtering for Suspicious Patterns: To identify the most suspicious coordination patterns, we apply multiple filters designed to isolate synchronization on illiquid stocks with abnormally high returns. We retain only trader pairs meeting all of the following criteria:
- Stock illiquidity: The stock was traded by fewer than 100 unique traders during the analysis period.
- Low trading volume: The stock had total trading volume below 1,000 trades during the analysis period.
- Low trader activity: Each trader in the pair executed fewer than 1,000 total trades during the analysis period, filtering out hyperactive traders for whom coordination is more likely coincidental.
- High profitability: The average profitable return when coordinating exceeded 20%, substantially above typical market returns.
These filters isolate trader pairs who repeatedly coordinate on multiple rare, illiquid stocks and achieve exceptional returns — a pattern consistent with advance knowledge of material non-public information rather than coincidental overlap or public information trading.
These filters are intentionally conservative and designed to isolate only the most extreme coordination patterns. If such patterns were purely coincidental, we would expect few or no persistent networks to emerge across independent time periods. The fact that similar network structures appear in both years suggests that the results are not driven solely by threshold choice.
Network Construction and Analysis
We construct an undirected network graph where nodes represent traders and edges represent coordination relationships. An edge connects two traders if they coordinated on at least one stock meeting the filtering criteria above. Each edge is weighted by the average profitable return achieved across all stocks on which the trader pair coordinated. This network structure allows us to identify clusters of traders who consistently coordinate with each other on profitable trades, potentially indicating information-sharing networks.
For each trader in the network, we calculate:
- Degree: The number of other traders with whom the trader coordinated.
- Degree centrality: The proportion of all possible connections the trader has, normalized by network size.
- Node return: The average profitable return across all coordination edges connected to the trader.
We also identify connected components — isolated subgraphs of traders who are connected to each other but disconnected from the rest of the network. The largest connected component represents the primary cluster of coordinated traders.
To visualize the relationship between traders and stocks, we construct a bipartite network where trader nodes and stock nodes are connected by edges representing trading activity. This representation reveals which stocks serve as common coordination points across multiple traders, highlighting potentially suspicious securities that may be the subject of insider information.
Analysis Period
We conduct separate analyses for calendar years 2023 and 2024 to identify temporal patterns in coordinated trading behavior and assess whether suspicious networks persist across different time periods.
Results
2023 Analysis
For the period from January 1, 2023 to December 31, 2023, our filtering criteria identified a network of synchronized traders operating on illiquid stocks with abnormally high returns. The resulting trader network exhibits specific structural properties that suggest information sharing rather than coincidental simultaneous positioning.
Network Structure and Centrality
Figure 2 presents the relationship between network position and trading performance. The top panel shows average profitable returns as a function of degree (the number of coordination partners each trader has). We observe a weak positive relationship (β = 0.0111, p = 0.4148), though not statistically significant. The bottom panel examines the relationship with degree centrality (the proportion of possible connections each trader maintains). Here we find again a positive relationship (β = 1.3305, p = 0.4148), still not reaching statistical significance.
While these relationships do not achieve statistical significance, the positive coefficients suggest a pattern where more connected traders tend to achieve higher returns, consistently with the findings in the literature.
Most Profitable Coordination
Figure 3 illustrates the most profitable coordinated trade identified in 2023: two traders executing synchronized long positions in Fusion Fuel Green PLC (ISIN: IE00BNC17X36) during November 2023. The green marker indicates the timing of coordinated entry, positioned ahead of a dramatic price surge from approximately $25 to over $55 in December 2023 — a return exceeding 100% within one month. This trading pattern is difficult to reconcile with standard momentum-based or technical trading strategies. Fusion Fuel Green PLC is a thinly-traded renewable energy company with limited analyst coverage and minimal retail investor attention, making simultaneous independent discovery by multiple traders unlikely.
Largest Coordination Network
Figure 4 shows the stock with the most coordinated traders in 2023, Semler Scientific, Inc. (ISIN: US81684M1045), where six traders took long positions in November 2023. The synchronized entry (indicated by the green marker) preceded a steady upward trend from approximately €29 to €46 by late December 2023, representing a ~59% return. While less dramatic than the Fusion Fuel example, the aligned behavior of six independent traders on this small-cap healthcare technology and diagnostics company — again, not a typical retail favorite — ahead of a sustained price increase could indicate advance knowledge of positive developments.
Network Topology
Figure 5 presents the bipartite network structure of the largest connected component in 2023. Blue circles represent traders, while orange squares represent stocks. This visualization reveals two stocks — Twist Bioscience Corporation and CytoMx Therapeutics, Inc. — connecting multiple traders. Both companies operate in the biotechnology sector, suggesting potential information flow from sources with access to biotech industry developments.
2024 Analysis
Analysis of the 2024 trading period reveals similar patterns, with some notable differences in network structure and the stocks serving as coordination points.
Network Structure and Centrality
Figure 6 presents network metrics for 2024. The relationship between degree and average returns (top panel) shows a positive but not statistically significant relationship (β = 0.0068, p = 0.7822). The relationship with degree centrality (bottom panel) is similarly positive but not significant (β = 1.2122, p = 0.7822). Notably, the 2024 network exhibits higher baseline returns (30-50% compared to 30-35% in 2023) but with wider confidence intervals, suggesting greater heterogeneity in coordination profitability.
Most Profitable Coordination
Figure 7 shows the most profitable 2024 coordination: five traders entering long positions in Vuzix Corporation (ISIN: US92921W3007) in December 2024. The entry preceded a dramatic price surge from $2.50 to over $5.50 — more than doubling in value within weeks. Vuzix, a manufacturer of augmented reality devices, represents another example of synchronized trading on an obscure technology company with limited mainstream attention.
Largest Coordination Network
Figure 8 presents the stock with the most coordinated traders in 2024: Reconnaissance Energy Africa Ltd (ISIN: CA75624R1082), involving seven traders who entered long positions in May 2024. The entry occurred at a price of around CA$0.90, followed by a sustained rally to CA$1.45 by June 2024. Reconnaissance Energy Africa, an oil and gas exploration company operating in Namibia, represents yet another thinly-traded stock in a specialized sector unlikely to attract coincidental simultaneous retail trader attention.
Network Topology
Figure 9 shows the largest connected component in 2024, again structured around two coordination hub stocks: Reconnaissance Energy Africa Ltd and Chesapeake Gold Corp. The network topology is similar to 2023 — multiple traders connected through common trading of two stocks in related sectors (in this case, natural resources exploration). Interestingly, the sector focus shifts from biotechnology (2023) to natural resources exploration (2024), suggesting that the information sources feeding these networks may vary across time, or that different information networks operate in different sectors.
Discussion
The consistency of the results across 2023 and 2024 — despite partially different stocks and traders and varying market conditions — suggests that we are observing a pattern of information sharing rather than coincidental synchronicity. The positive relationship between network centrality and returns, while not statistically significant, appears in both years with consistent directionality, suggesting limited statistical power rather than absence of effect.
Alternative explanations merit consideration:
Influencer following: Traders might be following influential platform personalities. However, popular influencers typically discuss well-known stocks rather than obscure small-caps, and would produce larger, more visible coordination networks than we observe.
Shared analytical approach: Independent analysis using similar frameworks might produce synchronization. However, this cannot explain the precise timing we observe — multiple traders entering simultaneously at price inflection points on thinly-traded stocks with limited public information.
Platform algorithms: Recommendation systems might suggest similar stocks to similar users. However, platforms typically recommend high-volume stocks, not the illiquid securities we identify, and algorithmic recommendations would produce more diffuse timing patterns.
More granular temporal analysis using daily or hourly trade data could reveal whether coordination occurs simultaneously or sequentially, informing whether traders receive information from common sources or from each other. Cross-platform analysis could determine whether networks span platforms or remain platform-specific. Linking coordinated trades to subsequent news announcements or corporate events would provide more direct evidence of information advantage.
Appendix
The network analysis showed that predicting specific instances of insider trading is not possible, as coordination events are extremely rare and typically occur only once per trader pair, each time involving different stocks, leaving no historical pattern of repeated coordination from which to predict which traders will coordinate again or which stocks they will target next.
We can nevertheless leverage the collective signal from subsets of high-performing traders to predict future price movements of individual stocks. In the rest of this section, we develop and validate a machine learning model to predict the 30-day-ahead return of individual stocks using a set of indicators that capture changes in positions and trade volumes across a set of selected traders. The historical correlation between the change in a trader’s position and the 30-day-ahead return is used to identify the traders whose activity is most predictive of future price movements.
A trader’s position on a given day is defined as the cumulative number of buy trades minus the cumulative number of sell trades prior to and including that day. The change in a trader’s position is defined as the average position over the previous 30 days minus the average position over the previous 90 days: this increases when buy trades outnumber sell trades - indicating that the trader is taking a long position on the stock - and decreases when sell trades outnumber buy trades - indicating that the trader is taking a short position on the stock.
The following indicators are used as features to predict the 30-day-ahead return:
- Buy Trades Change: Change in the 30-day average of daily total buy trades compared to the 90-day average.
- Sell Trades Change: Change in the 30-day average of daily total sell trades compared to the 90-day average.
- Long Traders Change: Change in the 30-day average of daily counts of traders with more buy than sell trades compared to the 90-day average.
- Short Traders Change: Change in the 30-day average of daily counts of traders with more sell than buy trades compared to the 90-day average.
- Largest Buy Trade Change: Change in the 30-day average of the largest number of buy trades executed on a single day compared to the 90-day average.
- Largest Sell Trade Change: Change in the 30-day average of the largest number of sell trades executed on a single day compared to the 90-day average.
- Largest Buy Weight Change: Change in the 30-day average of the largest portfolio weight for a buy trade compared to the 90-day average.
- Largest Sell Weight Change: Change in the 30-day average of the largest portfolio weight for a sell trade compared to the 90-day average.
- Best Performance Change: Change in the 30-day average of the best trade performance compared to the 90-day average.
- Worst Performance Change: Change in the 30-day average of the worst trade performance compared to the 90-day average.
We use the above features to train a CatBoost classifier which predicts the sign of the 30-day-ahead return (class 1: positive return, class 0: negative return) of a given stock. The hyperparameters used for training the CatBoost algorithm are reported in Table A1.
| Hyperparameter | Value |
|---|---|
| objective | Logloss |
| iterations | 100 |
| boosting_type | Ordered |
| grow_policy | SymmetricTree |
| has_time | True |
| l2_leaf_reg | 3 |
| subsample | 0.7 |
| rsm | 0.7 |
We backtest the model by training it on each day from January 1, 2025 to September 30, 2025 using the most recent 5 years (5 x 250 days) of data, and using the trained model for predicting the probability that the stock price will go up over the subsequent 30 days. For each stock, we extract the features using only the activity of the traders whose position changes have at least 10% correlations with the 30-day-ahead stock return over the 5-year training set.
The results indicate that the model has good predictive ability on a set of highly traded ISINs, as both the accuracy score and ROC-AUC score are above 60% (Figure A1, Figure A2, Figure A3 and Figure A4). Further research is needed to assess whether these results generalize across a broader set of stocks and over different time windows.