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Improving the predictability of stock returns with Bitcoin prices

机译：利用比特币价格提高股票回报的可预测性

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This paper examines the role of Bitcoin prices (BTC hereafter) in stock return predictability of G7 countries. For completeness, the existing predictive models for stock returns that account for both country-specific and common factors are singly and jointly compared with BTC-based predictive model. The analyses are conducted for both in- and out-of-sample forecasts with multiple forecast horizons. The following results are evident in the paper. First, the paper fords evidence in favour of BTC in terms of individual in-sample forecast performance albeit the reverse for interest rate. However, the common factors appear to compete fairly with Bitcoin as the former consistently outperform the latter in three countries. Second, in terms of forecast combination, the stock returns of the G7 countries are better predicted by BTC-based model than their respective macroeconomic variables combined except for Japan. Third, while the out-of-sample forecast results for short forecast horizon reflect those obtained for the in-sample period; the performance however seems to diminish over longer forecast horizon. This is underscored by the fact that investors in the Bitcoin market often speculate over short periods due to the market tendency for higher volatility (risk). Fourth, we also test whether accounting for structural breaks matters in the BTC-based predictive model and the results largely suggest otherwise except for the out-of sample forecast of USA. Overall, the predictive power of Bitcoin can be exploited when modelling stock returns particularly over the period coinciding with high volumes of Bitcoin transactions.
机译：本文探讨了比特币价格（BTC以下）在G7国家的股票回报率中的作用。为了完整性，与基于BTC的预测模型相比，对股票的现有预测模型占据了国家特定和常见因素的账户。对具有多个预测视野的样本内预测的分析进行了分析。以下结果在论文中是显而易见的。首先，纸张有利于BTC的证据，以便个体预测性能，尽管利率相反。然而，常见的因素似乎与比特币相当竞争，因为前者在三个国家始终如一地优于后者。其次，就预测组合而言，基于BTC的模型比他们各自的宏观经济变量更好地预测了G7国家的股票回报，除了日本。第三，虽然短期预测地平线的样本外预测结果反映了对样品中获得的那些;然而，性能似乎在更长的预测地平线上削弱了。这一事实是，由于市场趋势，比特币市场的投资者在短期内猜测较高的波动性（风险），这是强调的。第四，我们还测试了基于BTC的预测模型中结构性中断的核算和结果，结果在很大程度上提出了美国的超出样本预测。总的来说，在造型股票收益率时，可以利用比特币的预测力量，特别是在与大量比特币交易中恰逢一致。

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