Short Straddle Research Articles

Supervised Machine Learning Classification for Short Straddles on the S&P500

In this paper, we apply machine learning models to execute certain short-option strategies on the S&P500. In particular, we formulate and focus on a supervised classification task which decides if a plain short straddle on the S&P500 should be executed or not on a daily basis. We describe our used framework and present an overview of our evaluation metrics for different classification models. Using standard machine learning techniques and systematic hyperparameter search, we find statistically significant advantages if the gradient tree boosting algorithm is used, compared to a simple “trade always” strategy. On the basis of this work, we have laid the foundations for the application of supervised classification methods to more general derivative trading strategies.

Choosing the right options trading strategy: Risk-return trade-off and performance in different market conditions

The investment decisions are subjected to risk and return of the financial asset. Options strategies help employ a suitable strategy to balance the risk-return trade-off. The study analyzes the risk-return trade-off of the long straddle, long strangle, long call butterfly (LCB), short straddle, short strangle, and short call butterfly (SCB) strategies. Moreover, it measures the impact of strategy risk and options premiums on strategy return using panel data analysis. Additionally, the study evaluates the performance of options strategies using the excess returns to risk approach under neutral and volatile market conditions. This paper considered companies of top-six sector indices of the National Stock Exchange from 2009 to 2020 and collected data of 18,720 option contracts and 3,744 observations for each strategy (22,464 observations). The study revealed that risks of long straddle and long strangle strategies have a positive impact, and options premiums negatively influence their payoff. ATM call premiums positively affect LCB payoff, while OTM and ITM call premiums positively influence SCB payoff. However, the risks of butterfly strategy did not influence its payoff. The risk of short straddle and short strangle strategies negatively influenced the payoff and were considered riskier strategies. Moreover, short straddle and short strangle strategies enhanced excess returns under both market conditions. The results would help the investors in choosing the appropriate strategy by analyzing the impact of risk on the payoff and the ability to enhance excess returns to the risk of various options strategies to incorporate in their investment.

Angled Short Straddle: A New Dimension of Trading

In this article, the authors propose a novel option strategy that is better (in terms of success rate, profitability, risk, and return) than the short straddle option strategy on the Indian stock market index, Nifty. By using the trigonometric ratio tan θ, an angle is formed between the monthly points earned/lost by the short straddle option strategy and the monthly change in the price of the Nifty index. Further, the angle for the next month is forecast by using the technique of the 14-month moving average. After calculating the forecasted angle by the 14-month moving—average technique, the related angle (of forecasted angle) in different quadrants is calculated. The angles of quadrants one and two are considered because short straddle is profitable in quadrants one and two and hence the angles of quadrants three and four have been ignored. By using the total straddle premium of the forecasting month, the upside and downside levels of Nifty of that month were calculated where this forecast angle will form. After finding the upper level and lower level of Nifty, the authors propose a strategy (angled short straddle strategy) that involves the selling of a call and put option. They compare the proposed strategy against the short straddle option strategies based on the success rate, overall profitability, risk, and return in the Indian stock market. The result indicates that the proposed strategy has an 18.06% higher success rate than the short straddle strategy. In terms of overall profitability, the proposed option strategy is more profitable by 57.22% than the short straddle strategy. They also compare the risk and return of both strategies, and their results show that the proposed strategy has lower risk and higher return than the short straddle option strategy; these results were verified by the regression analysis. In the end, the results of the success rate have been verified by the time-series plot of India VIX and realized volatility. Due to the higher success rate and better profitability, the proposed option strategy can be consistently applied by any trader on the Nifty for profit generation. <b>TOPICS:</b>Options, emerging markets, performance measurement, wealth management <b>Key Findings</b> ▪ The angled short straddle option strategy is more successful and profitable than the short straddle option strategy in the Indian stock Index (Nifty). ▪ The Nifty index has less bullish than bearish volatility behavior. ▪ The Nifty index has range-bound properties. ▪ This is the first article where trading results are based on angle tan θ.

Golden Chariot Capital’s Foray into Option Trading

The case presents a real-life situation faced by a research analyst to improve the performance of the funds under management by exploring the opportunities in the options market. Golden Chariot Capital (GCC), an investment firm with ₹500 million worth of assets under management, was failing in its objective to provide long-term capital appreciation with a steady income to its investors. GCC had its funds invested in publicly traded common stocks and corporate bonds. In the last 8 months, GCC failed to match up with the benchmark index. Ms Indira, a research analyst at GCC, was given the task to identify and suggest alternate avenues of investment. Indira brought forward a proposal to explore the derivatives market in general and options market in particular to improve the fund performance. After going through Indira’s proposal, few fund managers at GCC were reluctant to expose their funds to the speculative market of options. Hence, Indira was asked to conduct a pilot study on the payoffs resulting from selected income strategies using options. As an illustration, Indira came up with five income strategies comprising covered call, covered put, short straddle, short strangle and long iron condor that involved either selling of options resulting in income or reduction of cost of the portfolio. The case will help the students to learn about options and their payoffs, as well as strategies involving various options. The case is equally useful for practitioners taking a balanced view of the market to develop appropriate options related to income strategies.

Return Analysis on Contract Option Using Long Straddle Strategy and Short Straddle Strategy with Black Scholes

Investment in the capital market never had any risk. The greater the risks level of an asset, the greater the risk of the asset, and vice versa. One of the risks is the fluctuated stock price movement. Derivatives instruments can be used to mitigate the risk of investment. One of the derivatives instruments traded in Indonesia is option contract. This study aimed to analyze the return on contract option using long straddle strategy and short straddle strategy with Black Scholes model. In addition, this study also compares which strategies are better between the two strategies. The sample in this research was determined by purposive sampling method to obtain 5 companies as a sample. Based on the results, long straddle strategy is the best strategy because the profits are unlimited and the loss is limited to call and put premium. For investors who want to invest in the derivatives market, in particular contracts option investors should look at the movement of stock prices. If the movement of stock increased or decreased drastically, then the best would be long straddle strategy. In addition, investors should choose an option contract with a validity period of 2 months because of the potential to provide greater profits.

Nifty Volatility Using Neutral Strategies

This research analyzes derivative strategies that have been applied on the Nifty IT index, which is one of the sectoral indexes under Nifty. It involves the application of derivative strategies on five years of historical data of the Nifty IT index and determines their profitability. Two strategies have been used for this purpose: long straddle and short straddle. The success rates of the two neutral option strategies have been computed. The research aims to identify the strategy that can be consistently applied on the Nifty IT index in the future to generate profits. Payoff graphs for the most recent months have also been generated to complement the findings of this analysis. The results suggest that the success percentage of a long straddle strategy has been around 40% and that of short straddle around 60%. The authors conclude that the market is more bearish on volatility for the Nifty IT index. The short straddle strategy was more successful than the long straddle strategy for the period studied. Therefore, of the two strategies, the short straddle seems to offer the best prospects for neutral trading on the Nifty IT index in the future.

Factors that affect the performance of distressed securities hedge funds

We present a four factor model to replicate distressed securities hedge fund returns. The model considers the returns of short put options, a short straddle on bonds, the spread between high yield and Treasury bonds, and stocks with small market capitalization. On the basis of this model, we conduct a multivariate analysis of how fund characteristics affect risk-adjusted performance. A high-water mark and performance-based compensation are positively related to risk-adjusted performance, which is in line with much of the hedge fund literature. In contrast to other work, however, we find that lock-up is negatively related to performance and that fund age is unrelated to performance. Our work provides a better understanding of the nature and critical factors for the success of distressed securities hedge funds.

Market risk exposure of merger arbitrage in Australia

AbstractWe investigate the risk‐return characteristics of merger arbitrage in the Australian market for corporate control, whereby hedge fund managers acquire companies subject to a takeover offer. On average, a strategy of buying target companies and short‐selling bidders making scrip offers would have generated an annual return of 30 per cent from 1985 to 2008, excluding transaction costs, compared to the return on the broader market of 12 per cent. However, performance is not market neutral, being positively associated with market returns during downturns and inversely related to market movements during rising markets. The payoffs to this strategy are analogous to a short straddle, whereby the investor is short a call and put option at the same exercise price. These results are consistent with large‐sample evidence from the United States and the United Kingdom and have not previously been documented in Australia, in which prior evidence is based only on cash deals during the 1990s.

Effective options trading strategies based on volatility forecasting recruiting investor sentiment

This study investigates an algorithm for an effective option trading strategy based on superior volatility forecasts using actual option price data for the Taiwan stock market. The forecast evaluation supports the significant incremental explanatory power of investor sentiment in the fitting and forecasting of future volatility in relation to its adversarial multiple-factor model, especially the market turnover and volatility index which are referred to as the investors’ mood gauge and proxy for overreaction. After taking into consideration the margin-based transaction cost, the simulated trading indicates that a long or short straddle 15days before the options’ final settlement day based on the 60-day in-sample-period volatility forecasting recruiting market turnover achieves the best average monthly return of 15.84%. This study bridges the gap between option trading, market volatility, and the signal of the investors’ overreaction through the simulation of the option trading strategy. The trading algorithm based on the volatility forecasting recruiting investor sentiment could be further applied in electronic trading and other artificial intelligence decision support systems.

Empirical Properties of Straddle Returns

An at-the-money (ATM) straddle, i.e., going long an ATM call and an ATM put with the same maturity, is generally thought of as a volatility trade. It is essentially delta-neutral, but a large price move in either direction or an increase in implied volatility will produce a profit. A delta-neutral straddle position also has zero beta, so under the CAPMit should earn the riskless rate. Research has shown, however, that straddles with stock index options tend to lose money, which may be attributed to a volatility risk premium: it is the cost of hedging against a rise in volatility. If buying straddles produces losses, writing straddles should yield excess profits. An important aspect of the trade is that the delta (and beta) of the position change when the underlying index moves away from its initial level, and rebalancing is necessary if one wishes to maintain neutrality. In this article, Goltz and Lai examine the performance of buying and holding one-month straddles on the DAX index, with and without rebalancing, and find negative returns on average. If investors are entering the trade as a volatility hedge, one might expect the return to vary with other measures on volatility risk and potential hedging demand. They find that a widening credit spread on corporate bonds relative to government bonds, greater stock market turnover, and higher actual volatility all are related to straddle returns. But in considering what position an investor with constant relative risk aversion would take in straddles as part of an optimal portfolio including the underlying stock index and the riskless asset, they show that for risk aversion over a broad range, the optimal position would be to short straddles. That is, the “risk premium” in the market is too big to be consistent with utility maximization by investors with a reasonable level of risk aversion. The effect is most important for daily rebalancing, but that requires bearing heavy transaction costs, to the point that the potential improvement in utility would be largely wiped out in trying to capture it in the market.

A Hybrid Derivative Trading System Based on Volatility and Return Forecasting

The following proposes a methodology that utilizes a generalized regression neural network to develop a hybrid option trading system that incorporates both volatility and return forecasting. This study focuses on the S&P 500 stock index as being representative of the market. Two different hybrid systems are discussed. The first hybrid system applies a signal from the volatility forecasting as a primary signal and then uses long and short straddle, strip, and strap strategies to take advantage of the volatility signal. The second hybrid system applies a signal from the return forecasting as a primary signal and then uses long calls and puts and bull and bear spread strategies to take advantage of the forecasting signal. The results show that the hybrid options trading model can improve the overall trading return and can outperform trading models using merely return forecasting or volatility forecasting in isolation. A sensitivity analysis for each trading system is investigated to observe the results for different values of critical option parameters.

Hedge Fund Payoffs and Loss Aversion

For an agent with loss averse preferences we derive the optimal payoffs with one option. A total of four different payoffs are found to be optimal, depending on the strike price of the option and whether the initial position of the agent is one of surplus or shortfall. Our results have implications for the hedge fund industry, where funds typically display nonlinear payoffs. Manager compensation typically includes a high-water mark for the incentive fee, which is a likely candidate for the reference point in loss averse preferences. The shape of the optimal payoffs for an initial shortfall position corresponds either to a short put or short straddle. This can be related to managers that are below their customary return, suggesting that investment strategies creating a short put payoff like those followed by LTCM might be driven by loss averse preferences. Furthermore, the steepness of the payoffs under loss aversion increases in the difference to an initial reference point, which corresponds to hedge funds increasing their risk when performance falls further behind.

Predicting Implied Volatility in the Commodity Futures Options Markets

Both academics and practitioners have a substantial interest in understanding interest in understanding patterns in implied volatility that are recoverable from commodity futures option. Such knowledge enhances their ability to accurately forecast volatility embedded in these high risk option. This paper examines option-implied volatility contained in the heavily traded September corn futures option contracts for ten-year period, 1991-2000. We also test whether a “weekend effect” exists in the market for this contacts. We evaluate the performance of various measures widely employed in the literature to estimate historical volatility. We further report the nature of profit from a short straddle strategy which seek to exploit differences between option-implied and historical volatility.

Hedging Price Risk with Options and Futures for the Competitive Firm with Production Flexibility

When some input decisions can be made after price is realized, separation between production and hedging decisions still holds only under limited circumstances. Under the assumption of a restricted profit function that is quadratic in price, the optimal futures hedge of a risk-averse firm equals expected output and a short straddle position is desirable assuming that futures and options prices are unbiased. In this case, the use of options not only raises expected utility by reducing income risk but also affects the firm's input decisions in general. Copyright 1992 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.