We are proceeding with the analysis by analyzing the data well, finding the one who is logically strange before and after the shareholder benefit, and thinking that we can make money at that timing.
Now, while pulling all the stock price data since 2000, there is a wait, so let's take a look at the mechanism of the preferential treatment during that time. So, let's take a look at the analysis procedure and the aim.
By the way, stocks are almost beginners who buy only their favorite companies. And, as the title says, it's just a ** concept **, so the actual code doesn't come out.
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The first two points I thought about were the following.
--Credit sale on the vesting date of shareholder benefits. Collected on the falling day. --Buy order on the ex-rights day of shareholder benefits. Sell order near the next vesting date
If it is 1, the risk of credit selling will increase, but the risk of holding stocks in the medium term seems to be just as large (there are many things such as the bursting of the bubble in China), so the shorter the period, the better the results can be seen. It's fun, so I'll try it with 1.
2 can be assembled with the same logic, so it might be a good idea to implement it. Of course there is a risk of holding shares in the medium term, though.
When I write so far, I feel like I hear a voice saying ** "You can do it without analyzing the data" **. That's not the case. ..
In the world of finance, there are three important indicators, each of which has something like risk, return, and expected value (theoretical value) of return. So, by analyzing the data, ** the stock with the closest return to the theoretical value ** can be extracted with the minimum risk.
To put it in more detail, it will be possible to ** list stocks that are almost certainly profitable **. In my case, the risk of getting money is finite, so I have to do something like this. ..
Therefore, this time as well, analysis to find stocks that are likely to be profitable.
The best thing I know is to standardize the return index with the Nikkei 225 return index and look at the cycle of fluctuations before and after the preferential treatment date. Then, ** Extract the day when the difference is large between the day before the special treatment day and the day after the special treatment day **.
Considering the fluctuation range and the minimum number of transactions, we will calculate the expected return value (average value) and the return volatility (degree of dispersion / variation).
The data obtained by this analysis is
--The day to buy --Day to sell --Expected return --Return volatility (risk)
Wonder?
After that, you can purchase it considering the return you want and the risk. If it seems to be a hassle to do with the eyes, I will apply it to the standard normal distribution
Ideally, I think it would be better to calculate the distribution of the days to buy and the days to sell, taking that into consideration.
I would be very happy if I could automate this. I haven't been able to eliminate the risk of the Nikkei 225 up and down, but I wonder if I can accept it. ..
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