Monte Carlo Stock Price Forecasting and Portfolio Optimization

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Presentation transcript:

Monte Carlo Stock Price Forecasting and Portfolio Optimization By: Garrett Ashbaugh Advisor: David Shaffer

Overview Background Previous Work My Project Results and Analysis Questions

Background Efficient market Inefficient market Technical Analysis Impossible to beat market because stocks reflect all available information Inefficient market Stock prices not always accurately priced Technical Analysis Use historical data to forecast future stock returns

Previous Work Amman Stock Exchange, Dima Alrabadi and Nada Aljarajesh 2012 Compared Monte Carlo Simulation to Simple and Exponential Moving averages (2003-2012) Monte Carlo Simulation correctly predicted their market Monte Carlo Simulation, Magnu Erik Hvass Pedersen 2015 Compared Monte Carlo results of 3 companies to the S&P 500 index Optimized portfolio by using mean-variance and geometric mean

My Project 3 parts: Quantopian Hypothesis: Monte Carlo Simulation Portfolio Optimization Trading Quantopian Hypothesis: Method will yield higher returns than the market

Monte Carlo Simulation Created a list of past 30 returns for a stock Ran a projection 10 time steps into the future by taking a random sample for each time step Today_price = Yesterday_price * e^(sample_return)

Monte Carlo Simulation cont. Ran the simulation 1000 times Took the end price of the simulation and averaged them all together

Difference = Projected Price – Yesterday Price Sortino Ratio 1/03/06 Yesterday Price Projected Price Difference Sortino Ratio ARNC 29.56 30.75 1.19 0.27 BAC 46.11 46.54 0.43 0.06 MRK 31.78 32.90 1.12 0.08 PFE 23.27 24.13 0.86 JPM 39.65 40.26 0.61 0.11 Difference = Projected Price – Yesterday Price Sortino Ratio Assets standard deviation of negative asset return Harmful volatility

Portfolio Optimization Started with universe of Dow Jones Industrial Average Ran Monte Carlo Simulation Took top 10 stocks with the highest Sortino Ratio Out of that 10 took top 5 stocks with highest difference % of portfolio was based on Sortino of the individual stock/total Sortino ratio of the 5 stocks

Total Sortino Ratio = (.27+.06+.08+.08+.11) = .60 1/03/06 Yesterday Price Projected Price Difference Sortino Ratio ARNC 29.56 30.75 1.19 0.27 BAC 46.11 46.54 0.43 0.06 MRK 31.78 32.90 1.12 0.08 PFE 23.27 24.13 0.86 JPM 39.65 40.26 0.61 0.11 ARNC Sortino Ratio = .27 Total Sortino Ratio = (.27+.06+.08+.08+.11) = .60 ARNC % Of Portfolio = .27/.60 = .45 = 45%

Trading Gave the simulation $100,000 to trade with Buy and rebalance every week (Monday) If stock dropped out of top 5 then sold all the shares If stock was still in top 5 we bought/sold shares to reach desired % Only bought if the Sortino Ratio was positive

Results Back tested at 3 different time periods (DOW) 2002-2005 Market fluctuated but ended around the same as it started 2006-2009 Market declined 2011-2014 Market increase

2002-2005

10440.07 10111.04

70.46% from low (18.7)% (11.3)%

2006-2009

10862.14 8447.53 (22.23%)

Peak = 16.58% 14.5% (24.4)% 2.6% Sortino Ratio -0.529

2011-2014

17827.27 11577.43 53.98%

From low 96% (6.6%) Total Return 53.98% Sortino Ratio 0.466 (16.3%)

Conclusions Decent predictor of the market Has Lags Possible changes: Have short term checks Short stocks if negative Sortino ratio Have regression checks against the whole index Take this algorithm to inefficient markets

Questions?

Works Cited Alrabadi, Dima and Aljarayesh, Nada. “Forecasting Stock Market Returns Via Monte Carlo Simulation: The Case of Amman Stock Exchange.” Journal of Business Administration (2015) Pedersen, Magnus Erik Hvass. "Portfolio Optimization and Monte Carlo Simulation." SSRN Electronic Journal SSRN Journal (2014): n. pag. Web. Staff, Investopedia. "Efficient Market Hypothesis - EMH." Investopedia. N.p., 28 Sept. 2016. Web. 18 Apr. 2017. Radcliffe, Brent. "Sortino Ratio." Investopedia. N.p., 26 Jan. 2016. Web. 18 Apr. 2017.