The goal of stock market forecasts is to predict how the stock value of a financial exchange will change in the future. Interest rates, politics, economic growth and many other factors affect the stock prediction. More accurate predictions lead investors to make more profit. Making decisions depends on the ability of foreseeing the stock market changes, consequently, digging out the role of randomness in financial time series behaviour and quantifying how predictable are financial time series is of great interest. This paper explores the limits of predictability on return’s dynamics using different stock markets. To determine the theoretical maximum prediction accuracy for the returns, we solve a limited case of the Fano inequality while taking in consideration some previous studies suggestion to avoid an incorrect and overestimated results of 81%. The findings of this study showed that returns predictability could reach 66% by measuring the entropy of four major American and Chinese indices namely Nasdaq 100, S&P 500, SSE and SZSE 500.
stock market, stock prediction, financial time series, predictability, returns
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