Profit from Time Decay in Options Using Deep Learning

Abstract

Sumit Kumar, Andrés Quintero and Joe Wayne Byers

One of the most flexible assets in the financial markets is the options, the flexibility makes it possible to create virtually unlimited strategies among different underlying assets, and combinations, contrary to the stock market, where the mechanism to sell or buy is relatively simple, other elements, like time can play against the trader. The derivative market could be overwhelming considering the broad alternatives, with high entry barriers and a steep learning curve. The new traders try to jump these barriers without a plan, hurrying to place trades that follow their emotions, and losing money along the process, as the best alternative to learn from trial and error. This research proposes an options trading framework that can help reduce this gap and profit from time decay.

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