In his presentation, Peter revealed how important are news and their interpretation for making money from trading on the stock exchange. Traders try to buy low after any market overreaction to bad news and sell high when the price moves back to the level before the news. The amount of information released every day about the companies, the economy and the markets themselves is enormous and difficult to process and interpret by a trader.
This Wednesday we were delighted to host the presentation of Peter Manolov, PhD. After graduating in Mathematics at the Sofia University, Peter got his PhD degree at the University of Illinois at Chicago. He continued his career with stints in the Institute for Mathematics and Informatics at the Bulgarian Academy of Science, and in Experian, where he worked on credit risk modelling. The highlight of the evening however was his experience as a quant in a hedge fund. There he was engaged with software support, data processing and most importantly, modelling the financial markets.
In his presentation, Peter revealed how important are news and their interpretation for making money from trading on the stock exchange. Traders try to buy low after any market overreaction to bad news and sell high when the price moves back to the level before the news. The amount of information released every day about the companies, the economy and the markets themselves is enormous and difficult to process and interpret by a trader. This is why some hedge funds try to predict the stock prices by employing models. Peter gave an example of a model for predicting the stock volume. His model predicts the volume of individual stocks by selecting a candidate variable set with a sample size of 10 trading days and estimating regressions on it. He used a moving window technique, moving the sample window one day ahead in order to gather enough forecasts (typically 1000) for the daily volume of each company. He further aggregated the forecasts for all the companies on the market and compared them to the observed values via the mean squared error and similar statistics. This error is compared to the error of another model with a larger sample size (e.g.11 trading days) until we find the model with the smallest prediction error for this variable set. The algorithm is repeated with different variable sets as well, until the ultimate money-making model is found. But as markets are constantly changing, so are the models – they are constantly re-estimated to keep track.
Modeling the financial markets is data-intensive and this is why Peter shared his experience in tackling big data problems in the second part of his presentation. As an example, he pointed out that his hedge fund received 100 GB zipped stock exchange data daily. Before employing it for modelling, he had to clean it. Typical data consistency problems like the presence of outliers arise because stock brokers and traders do not report their trades right after their execution but at the end of the day. A sudden jump and then drop in price data series is typically due to this reason. Outliers trimming that relies on quantile estimation and other data analysis techniques might be painfully slow with terabytes of data – for example, time series estimation took Matlab 14 days. This is why Peter developed his own Price Server solution that stored and compressed the same data so efficiently that it took only 6 hours to complete the 14-days Matlab challenge.
To end a memorable discussion in a memorable and original way, Peter sang on his guitar what he felt about an industry driven by people obsessed with making money. A picture is worth a thousand words, so a video should be worth a lot more , see on YouTube.
In what is becoming a tradition, we ended the evening with social drinks in a bar nearby.
If you want to be among the exclusive audience of such events in the future, do not miss our next presentation on 26.10.2014. Stay tuned for updates and join on our website, and social media twitter and Facebook page!
Written by: Vladimir Labov