Successful market timing strategies depend on superior forecasting ability. We use a sentiment index model, a kitchen sink logistic regression model, and a machine learning model (least absolute shrinkage and selection operator, LASSO) to forecast 1‐month‐ahead S&P 500 Index returns. In order to determine how successful each strategy is at forecasting the market direction, a “beta optimization” strategy is implemented. We find that the LASSO model outperforms the other models with consistently higher annual returns and lower monthly drawdowns.

中文翻译：

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结合预测和机器学习的市场时机

成功的市场时机策略取决于出色的预测能力。我们使用情绪指数模型，厨房水槽逻辑回归模型和机器学习模型（最小绝对收缩和选择算子，LASSO）来预测标准普尔500指数提前1个月的回报。为了确定每种策略在预测市场方向方面的成功程度，实施了“ beta优化”策略。我们发现LASSO模型的年回报率不断提高，而每月的提款额却更低，从​​而胜过其他模型。