Disclaimer: Lot of statistics and technical jargon ahead. Jump directly to the summary section for a quick walk-through. I'm neither a statistician or claim to be knowledgeable about the stock market in general. Use the information presented here at your own risk.
At Versa, we have been doing research into crunching network related data and providing actionable insight into network issues and network behavior in general. My wife works with financial models and a discussion on various tools used in econometrics led me into the rabbit hole of statistics and modelling (I had some experience with it back in school, but tech has come a long way in the past decade).
Although, I am fluent in Python, nothing came close rapid prototyping power of R, so had to pick it up along the way. After few datacamp courses, I had been trying to come up with a generalized statistical model that could work across a wide variety of similar data sets. There is a lot of literature that deals with model fitting and there are lot of libraries for number crunching (both in Python & R), however most of them were never designed for the deluge of data that we have now, nor do they deal with multiple seasonalities well (how the data looks per hour, per day, per week, per month, per year). After trying out stationary model fits and regressions, I moved on to time series models; These include a barrage of models, including Holt-Winters, Arima, ets, tbats, etc to fit a time series model. I have yet to try a LSTM to fit the data.
While browsing the interwebs, I stumbled across a generalized time series model fitter by Facebook's data science team: Prophet. It is fairly simple for use by an amateur like me and yields surprisingly good results. I decided to run this on Apple's daily opening price and tried to predict the motion of the stock for the next one year. There was no assumption on my part that this would yield any information on stock price (else statisticians would have been billionaires). What I wanted to figure out was the general direction of the stock's motion.
The prophet library allows the accounting for events affecting the stock price, which are mainly the holidays and the release announcements (and the days leading up to it). The following R code did the heavy lifting:
Here are some of the graphs generated by this model:
What does it all mean?
At Versa, we have been doing research into crunching network related data and providing actionable insight into network issues and network behavior in general. My wife works with financial models and a discussion on various tools used in econometrics led me into the rabbit hole of statistics and modelling (I had some experience with it back in school, but tech has come a long way in the past decade).
Although, I am fluent in Python, nothing came close rapid prototyping power of R, so had to pick it up along the way. After few datacamp courses, I had been trying to come up with a generalized statistical model that could work across a wide variety of similar data sets. There is a lot of literature that deals with model fitting and there are lot of libraries for number crunching (both in Python & R), however most of them were never designed for the deluge of data that we have now, nor do they deal with multiple seasonalities well (how the data looks per hour, per day, per week, per month, per year). After trying out stationary model fits and regressions, I moved on to time series models; These include a barrage of models, including Holt-Winters, Arima, ets, tbats, etc to fit a time series model. I have yet to try a LSTM to fit the data.
While browsing the interwebs, I stumbled across a generalized time series model fitter by Facebook's data science team: Prophet. It is fairly simple for use by an amateur like me and yields surprisingly good results. I decided to run this on Apple's daily opening price and tried to predict the motion of the stock for the next one year. There was no assumption on my part that this would yield any information on stock price (else statisticians would have been billionaires). What I wanted to figure out was the general direction of the stock's motion.
The prophet library allows the accounting for events affecting the stock price, which are mainly the holidays and the release announcements (and the days leading up to it). The following R code did the heavy lifting:
Here are some of the graphs generated by this model:
This is the graph of log(stock price) per day (This is done to normalize the variance of the graph). As you can see, even though we have a decent fit, the exact forecast is very klugy. We can't rely on the point forecast, but it does predict that after a small dip in early 2018, the stock will continue to grow up. Now let's look at each of the components and their effects:
The first graph shows that the stock will be on rise in 2018, but the curve shows sign of plateauing. The second component shows that the holidays and events have a fairly small effect on the stock price in general. The last graph shows that stock prices have been at their lowest near the end of January/early Feb (over the past 1 decade).
Another analysis we could do is to predict the growth of the stock, and by deduction, it's volatility. We repeat the same exercise for date vs gain in opening price:
Even though, there are few small spikes (which represent the upward movement of the stock), there are no downward spikes. Barring the temporal volatility of the market, AAPL is a solid stock to bank on. Doing the component analysis on the gain, we get:
Now the first graph here doesn't mean that the stock is going down, it points to the decrease in volatility. There seems to be a more pronounced effect of holidays and announcements on the gain observed, and stock dip is seen typically at the end of Jan/early Feb (as earlier graph predicted) on a Monday. September end, and June end/Early July seem like a good time to sell the stocks (on a Thursday).
What does it all mean?
- It is not a good time to buy APPL stocks right now. They are at an all time high.
- Jan end (on a Monday) might be a good time to pick up APPL stocks.
- June end (on a Thursday) might be a good time to sell these stocks.
- Since the stock is getting stabler, do not expect meteoric returns on investment.
- The author of this blog is not a statistician or a stock market guru, so ignore the first four points.
This is an interesting read. I have worked on a project where we had to check out the seasonal variations in the data.
ReplyDeleteBased on that, I was figuring out a good way to analyse stock market data for my own personal use (didnt expect to go further than visualization). I wanted to check how accurate a certain Company's predictions are :p
This blog will help me to get started!