Diversification and Market Neutral Portfolios in S&P500

Our goal is to investigate strategies to deal with the risks associated with holding asset in the stock market. We first deal with risk of holding a specific stock, by the use of diversification. Later, we’ll attempt to deal with the market risk, which is the risk of entire market going up and down. Data used in this project comes from daily adjusted closing price of stocks listed in the S&P500 index ranging from January 3rd, 2000 to December 31st, 2015 and the data is processed using statistical software R. Sections 2 through 4 of this paper demonstrate diversification and how to lower stock specific risk. Section 2 shows a case with two stocks in a portfolio. Moreover, the ideal portion of your budget allocated to each stock in the portfolio will be discussed. Section 3 scales up the discussion to twenty benchmark stocks in a portfolio. Creating thousands of potential portfolios was more difficult to compute, but it was necessary for Section 4. This part is the evaluation of how much money the portfolios create compared to just using the overall S&P500. Section 5 discusses calculating a stock’s beta, alpha, from Sharpe’s single index model and correlation with the S&P500, then calculate the same measures for the portfolio overall. By using those measures, we’ll attempt to make our portfolio neutral to the market. That is, on average, the portfolio’s value will change independent of the market in crisis situations. This part will deal with the market risk component and mix in with our diversification from above to also deal with stock specific risk

Exploring mispricing in the term structure of CDS spreads

YesBased on a reduced-form model of credit risk, we explore mispricing in the CDS spreads of North American companies and its economic content. Specifically, we develop a trading strategy using the model to trade out of sample market-neutral portfolios across the term structure of CDS contracts. Our empirical results show that the trading strategy exhibits abnormally large returns, confirming the existence and persistence of a mispricing. The aggregate returns of the trading strategy are positively related to the square of market-wide credit and liquidity risks, indicating that the mispricing is more pronounced when the market is more volatile. When implemented on the Markit data, the strategy shows significant economic value even after controlling for realistic transaction costs

Intraday Algorithmic Trading using Momentum and Long Short-Term Memory Network Strategies

Intraday stock trading is an infamously difficult and risky strategy. Momentum and reversal strategies and long short-term memory (LSTM) neural networks have been shown to be effective for selecting stocks to buy and sell over time periods of multiple days. To explore whether these strategies can be effective for intraday trading, their implementations were simulated using intraday price data for stocks in the S&P 500 index, collected at 1-second intervals between February 11, 2021 and March 9, 2021 inclusive. The study tested 160 variations of momentum and reversal strategies for profitability in long, short, and market-neutral portfolios, totaling 480 portfolios. Long and short portfolios for each strategy were also compared to the market to observe excess returns. Eight reversal portfolios yielded statistically significant profits, and 16 yielded significant excess returns. Tests of these strategies on another set of 16 days failed to yield statistically significant returns, though average returns remained profitable. Four LSTM network configurations were tested on the same original set of days, with no strategy yielding statistically significant returns. Close examination of the stocks chosen by LSTM networks suggests that the networks typically buy stocks that outperform during the formation period, mirroring a momentum strategy

Thin-trading effects in Beta : Bias v. estimator error.

Thin trading; Market model; Estimator; Bias; Variance; Cost;

Thin-trading effects in beta: Bias v. estimation error.

Market model; Thin trading;

The Spider in the Hedge

This paper provides an empirical study of the effectiveness of hedging the spider, a passive exchange traded fund (ETF) that replicates the S&P500 index. The spider is by far the largest ETF in the world: trading on the spider has grown so much during the past few years that it is now amongst the few most traded securities in the AMEX. The large net daily creation and redemption orders of recent years pose a problem to the market makers in the spider, as the orders may be too large to execute in the cash market. They face a decision about whether to hedge spider positions on their own book; and if so, how should they hedge? We have employed several sophisticated minimum variance estimates for the future hedge ratio, including OLS regression, an ECM to account for maturity effects and the cointegration of the spot and the future prices and, to the ECM residuals we apply EWMA and number of bivariate GARCH models to account for time-variation in the hedge ratio. We have applied these models to daily data for a 1-day rebalancing frequency and to weekly data for a 5-day re-balancing frequency, using data since the spider’s inception until the end of 2004. Marginal differences in the ‘optimal’ hedge ratios are apparent, but they are simply too small to have any significant effect on the hedged portfolio volatility. In out-of-sample testing we find that the naïve hedge where an equal and opposite position is taken in the future performs as well as the more technically sophisticated models, at both the daily and the weekly re-balancing frequency. Finally, we have considered the differences between hedging the spot index and hedging the spider. The efficiency of hedging the spider is superior to that of the index and the spider hedged portfolios have significantly lower volatility than the spot index hedged portfolios.