[Music] Alpha examples by data category part two I'm nitish Manny your partner in learning more about Quant Finance research by day I am the chief strategy officer at World Quant in my role I help to define the firm strategy as well as Drive several cross functional business initiat Ives including new avenues for growth and innovation quants in our last video we explored Alpha examples by data categories let's continue with the next set of categories the next category is sentiment data sentiment data quantifies the emotions of the masses towards the stock or Market in general the
data are captured from various mediums like social media channels news and blogs Quant researchers monitor popular opinion in an effort to seek to predict the direction of the movement in the stock price and the conviction or the intensity underlying these sentiments Quant researchers also analyze the sentiment Buzz that is the degree of activity of the investors in a particular stock let's review an example for such an alpha on brain of simulation platform remember at World Quant we Define an alpha as a a mathematical model that seeks to predict the future price movement of various financial
instruments the hypothesis for this Alpha idea is that if a Stock's sentiment Buzz is rising compared to historical average that means the stock is attracting higher investor attention lately and is possibly overpriced so we expect lower future returns and short the stock conversely we go long on stocks with falling anticipating higher future returns now let's implement this idea on world Quant brain with the proprietary expression Language by the way you can access all data categories discussed in this series on brain we compute the ratio of today's sentiment Buzz for a stock to the mean over
the last 10 trading days or two calendar weeks using the time series mean function values greater than one indicate an increasing Trend in the sentiment Buzz we apply a negative sign before the expression to express our bearish outlook for the stocks with increasing recent Buzz the back testing simulation runs for the previous 5 years to generate an alpha Vector for each day in the simulation if the value for the stock is negative it shorts the stock and goes long on the stocks with positive values we simulate the expression on the top 200 US stocks on
the basis of the liquidity and on delay one data to prevent any look ahead bias we neutralize the alpha over the industry apply a DK of 10 to smooth any noise in the buzz data and restrict the maximum capital on a single stock to 1% the results show a consistent sharp of 1 . 6 across years returns over 9% with a decent coverage across the selected [Music] Universe the last data category that we will explore is options options are contracts within the derivatives Market which give the right but not the obligation to buy or sell
an underlying security at a specific strike price while the world of options can be complex in this example we will focus on extracting information from Equity options by particularly focusing on a metric called implied volatility implied volatility is the expected future fluctuation in the price of the underlying stock implied volatility is computed using multiple variables like price of options time to expiry of options interest rates strike prices Etc higher option prices generally lead to higher implied volatility reflecting the demand for the option when the price of the stock moves above the strike price the holder
of the call option makes a profit when the price of the stock goes below the strike price the holder of the put option makes a profit for our analysis we use implied volatility derived from the at the money call and put options and at the money option is an option whose underlying asset price is very close to the Str stke price of the option the idea of our Alpha is based on capturing the difference in the demands of add the money call and put options as measured by their implied volatility over long Horizon to capture
longer term Trends in the stock prices this leads us to a hypothesis if implied volatility derived from call options expiring up to two years in the future is higher than the implied volatility derived from put options expiring up to two years in the future then the demand for a stock exceeds its Supply and we expect the stock prices to appreciate in the future we shall go long on such stocks for the stocks where the difference in the implied volatility is negative we have a bearish Outlook and we shall go short on them now let's discuss
how our idea is implemented using brains expression language we store our call and put options in two variables IV call and iv put respectively our Alpha captures the net demand of the stock as the difference in the implied volatilities we simulate the expression on the top 3,000 US stocks on the basis of liquidity and on delay one data to prevent any look ahead bias we neutralize Alpha over the industry apply a decay of seven to smoothen the signal and restrict the maximum capital on any single stock to 1% the results show a consistent sharp of
around two across years returns of 14% return to draw down ratio of three turnover of 25% and with decent coverage across the selected Universe to improve the alpha performance we can consider Trends in other variables from options trading like open interest and options volume which are indicative of the demand of the underlying stock in this video we explored sentiment and options data category with corresponding Alpha examples in our next video we are going to explore Alphas categorized by idea type let's continue our Quant research journey together quanton with me [Music]
