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- W4386034968 abstract "This paper employs a big data source, the Borsa Istanbul’s “data analytics” information, to predict 5-minute up, down, and steady signs drawn from closing price changes. Seven machine learning algorithms are compared with 2018 data for the entire year. Success levels for each method are reported for 26 liquid stocks in terms of macro-averaged F-measures. For the 5-minute lagged data, nine equities are found to be statistically predictable. For lagged data over longer periods, equities remain predictable, decreasing gradually to zero as the markets absorb the data over time. Furthermore, economic gains for the nine equities are analyzed with algorithms where short selling is allowed or not allowed depending on these predictions. Four equities are found to yield more economic gains via machine learning–supported trading strategies than the equities’ own price performances. Under the efficient market hypothesis,” the results imply a lack of “semistrong-form efficiency.”" @default.
- W4386034968 created "2023-08-22" @default.
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- W4386034968 date "2023-08-01" @default.
- W4386034968 modified "2023-09-30" @default.
- W4386034968 title "Big data–enabled sign prediction for Borsa Istanbul intraday equity prices" @default.
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- W4386034968 doi "https://doi.org/10.1016/j.bir.2023.08.005" @default.
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