![]() ![]() CCF for WSB mentions of $TSLA and its volume across 15 months, 12 months, 6 months, and 3 months (top left, top right, bottom left, and bottom right respectively).Īt first glance, WSB mentions appear to have predictive power over predict price. ![]() Similarly to the relationship between mentions and the stock price, the correlations also improved over shorter, more recent time frames, with even lower correlations at the tails (t = 0, r >. For all cases, the relationship had the most correlation when no shift was applied (t = 0, r >. The graphs of these relationships appear to be bell curves with negative kurtosis. When looking at Tesla and its CCF graph, we observe consistent results across all periods. The next step was to apply the same process to a stock’s mentions on WSB and its trading volume over the same periods. (t 0.3) CCF for WSB mentions of $TSLA and its adjusted close across 15 months, 12 months, 6 months, and 3 months (top left, top right, bottom left, and bottom right respectively). That correlation improved over shorter, more recent time intervals. The highest correlation is at (t = 0, r = 0.25) and when we move away from t the correlations rapidly become insignificant (t ≠ 0, r 0.2) to shifts in mentions on WSB over the past 15 months. The right graph shows the function between $QQQ mentions on WSB and its volume. The highest correlation is located around zero, and in this case, it is at lag = 3 (t = 3, r ~0.20). This graph represents other stocks in our basket, and though some tickers will have higher adjusted correlation coefficients, the patterns will remain similar. The left CCF graph is between WSB mentions of $QQQ and its price. These are the CCF functions graphed for $QQQ signals. It is implausible that so few retail investors could manipulate a fund whose constituent companies have a combined market cap of over $15 trillion. The average $QQQ mentions per trading day are 78.2 with a standard deviation of 71.7, and since Janumentions have peaked at 526. Our basket is mostly tickers in tech or tech-adjacent areas where we use $QQQ as a benchmark index for our basket.ĭerivatives trading through QQQ on WSB is uncommon and unlikely to influence the CCF of $QQQ signals. We made our basket to represent our goals while considering that r/wallstreetbets lacks in mentions data for many non-tech companies.īeyond company tickers, we include $SPY to consider derivatives trading in WSB and $SLV to determine the impact of the apparent silver “short squeeze” on WSB. These tickers are: This is our basket of 11 tickers, which we later examine. We apply CCF to a select basket of 11 tickers that represent various aspects of the stock market. Key terms: Cross-correlation function (CCF), lag, price action, r/wallstreetbets (WSB) mentions, volume Our primary goal is to discover the strength of the correlation between mentions on WSB and action in select stock tickers and identify any predictive power that WSB may have on the stock market particularly, to evaluate and quantify WSB’s impact on the recent short squeeze of $GME and related tickers. ![]() Thus adjusted correlation with negative lag shows predictive power, while the adjusted correlation with positive lag shows reactionary power. Our y-axis shows an adjusted correlation between two signals, while our x-axis shows the lag in trading days between the adjusted correlation of the two signals. We use time delay analysis to find correlations between mentions of tickers on Reddit trading forum r/wallstreetbets (WSB) and their price action (the change in the closing price of tickers on the stock market, in daily intervals) and volume (the number of shares traded in daily intervals). R WSB GME SERIESWe apply time delay analysis, a method of CCF where we determine the time delay between two series by finding a time “lag” where they are best correlated. Cross-correlation function (CCF) is a metric from signal processing that finds similarity between two series by measuring their displacement in increments. ![]()
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