Why would you buy an electric car on Jetski Friday? Or, a critique of financial markets from an options trading room

This article presents a close, dialogue-based ethnographic account of a group of contemporary options market makers making a decision about pricing options in Tesla, Inc. Careful attention to their deliberations reveals how the rise of algorithms and automation on financial markets have rendered traders alienated and estranged from the markets they work on for their livelihood. This alienation arises, in part, due to novel cascade effects between futures and underlying equities, which algorithmic and automated trading seems to afford, and which also relate to news events as well as the actions of politicians and prominent business people. Emerging from this alienation, traders produce a critique of how highly automated financial markets allocate capital and how ripe they are for political manipulation.

Reinforcement Learning for Options Trading

Reinforcement learning has been applied to various types of financial assets trading, such as stocks, futures, and cryptocurrencies. Options, as a novel kind of derivative, have their characteristics. Because there are too many option contracts for one underlying asset and their price behavior is different. Besides, the validity period of an option contract is relatively short. To apply reinforcement learning to options trading, we propose the options trading reinforcement learning (OTRL) framework. We use options’ underlying asset data to train the reinforcement learning model. Candle data in different time intervals are utilized, respectively. The protective closing strategy is added to the model to prevent unbearable losses. Our experiments demonstrate that the most stable algorithm for obtaining high returns is proximal policy optimization (PPO) with the protective closing strategy. The deep Q network (DQN) can exceed the buy and hold strategy in options trading, as can soft actor critic (SAC). The OTRL framework is verified effectively.

Market Trends and Options Trading: Viewpoint, Probability and Implications

In Dow theory, market trends are classified as secular trends for long-term frames, primary trends for medium-term frames, and secondary trends for short-term frames. For the long and medium terms, they can consist of major bull (bear) markets and minor bear (bull) markets; for the short terms, they may have corrections and bear rallies. These definitions of market trends are not very helpful to options traders because in practice, options trading is often done on a short-term time frame and options have a unique property of time value. Even in a bull market, there is a possibility of losing all money for buying call options; in a bear market, there is a probability of earning money for buying call options. This inconsistency often troubles options traders deeply. From the viewpoint of options trading, we introduce a new concept of analyzing the market trends and propose new methods for estimating the probabilities of the market trends since at any time the future prices are unknown. By simplifying the market trends into three concepts of uptrend, downtrend, and neutral trend, it will have consistent implications for options trading. JEL classification numbers: C13, G10, G13. Keywords: Market trends, Options trading, Trend probability, Estimation methods, Trading implications.

Spillover Effects of Stock Options Trading on the Volatility of Underlying Stocks

In this study, an investigation has been undertaken to determining the impact of Stock options trading on the volatility of underlying stocks. In order to achieve the objectives of the study, the researcher has taken into consideration six stocks on which options contracts were available throughout the reference period. The reference period of the study comprises of fourteen years that is divided into two periods namely, pre-derivatives and post-derivatives period. The pre-derivatives period comprises of seven years from 1995 to 2001 and it is the period during which stock options were not available in the stock market of India. The post-derivatives period also comprises of seven years (2002-2008) and it is the period during which stock options were available on the sample stocks. The researcher employed standard deviation to confirm the volatility effects of stock options and after the statistical analysis, the study affirmed an increase in the level of volatility in the post-derivatives period. Thus, it can be concluded, on the basis of the comparative analysis of statistical scores that the introduction of stock options trading have a definite impact on the volatility of the underlying stocks as the volatility levels registered upward trajectory throughout the post-derivatives period as compared to pre-derivatives period.

Option informativeness before earnings announcements and under real activity manipulation

Purpose This paper aims to investigate whether single-name options trading prior to earnings announcements is more informative when there exist real activity manipulations. Design/methodology/approach Using 5,419 earnings announcements during 2004–2018 made by 208 public US companies with relatively high options volumes ranked by the CBOE, the authors uncover two regularities using predictive regressions for stock return. Findings First, the total options volume up to twenty days pre-announcement is significantly higher than that in other periods only for earnings management firms; moreover, after detailing options characteristics, the authors find these intensive pre-announcement trading to be concentrated in transactions of in-the-money call and long-term maturity put options. Second, an increase in the single-name call minus put options volume can positively predict the underlying stock’s next-day excess return much better in real earnings management firms, with a larger magnitude of effect in periods right before regular earnings announcement dates. Originality/value This paper makes a marginal and novel contribution by showing that real earnings management can serve as a proxy for the potential profit from informed trading in options as the return predictability of options volume becomes stronger for firms that have the manipulation motive and indeed perform manipulative actions.