以機器學習技法探討貨幣利差市場投資組合策略及市場風險

本篇論文主要是探討機器學習演算法，利用股票市場以及原物料市場等，所萃取出來的特徵值，對於匯率市場投資組合的短期漲跌走向的預測能力。有別於傳統計量經濟的方法，本篇論文採用了隨機森林支撐向量機決策樹自適應增強算法，納入模混和模型中的三種模型。由這三種模型以及傳統經濟變數，機器學習演算法可以有效的預測市場違反鞅系統的時機點，進以返回交易訊號。由機器學習演算法所找尋到交易策略不僅給我們較好的預測正確率也同時擁有相較對照組有高的獲利能力。

關鍵字

機器學習；波動度；預測；貨幣投資組合；經濟特徵值；隨機森林；支撐向量機；決策樹自適應增強算法

並列摘要

This paper studies the forecasting capability of machine learning models with economic features. The machine learning model constructed is based on Random Forest, Support Vector Machine, Decision Tree with Adaptive Boosting, and Hybrid Model. With the information of past risk metrics, our models signify the predictability of the currency market instability. The predictability comes from the fact that our machine learning model observes the violation of martingale restriction in the currency market portfolio. Furthermore, we apply the resulting outputs from the model to the forex carry trading strategy. The profitability of the corresponding trading strategy is significantly higher than those from a long-term holding strategy and the benchmark strategy constructed by the VIX.

並列關鍵字

Machine Learning ； Economics Features ； Random Forest ； Support Vector Machine ； Decision Tree with Adaptive Boosting ； Hybrid Model ； Forex Carry ； Volatility Forecasting

參考文獻

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[3] Leo Breiman. Random forests. Machine Learning, 45(1):5–32, 2001.

[4] Yoav Freund and Robert E Schapire. A desicion-theoretic generalization of on-line learning and an application to boosting. In European conference on computational learning theory, pages 23–37. Springer, 1995.

[5] Tristan Fletcher, Fabian Redpath, and Joe D’Alessandro. Machine learning in fx carry basket prediction. In Proceedings of the World Congress on Engineering, volume 2. Citeseer, 2009.

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以機器學習技法探討貨幣利差市場投資組合策略及市場風險

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