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Amid the turbulent stock market today, the mysteries behind the fluctuations in corporate stock prices are worth delving into. Insider transfers reflect the company’s internal operations, management’s confidence in future development, and their assessment of the current economic environment. By analyzing the motives behind insider block trades, we aim to explore the relationship between stock prices and insider transfers. This article will utilize the TEJ API to conduct a quantitative analysis and focus on the impact of insider transfers on the stock prices of listed companies in Taiwan.
Based on the quantity and amount of transactions, securities trading meeting the following criteria must be reported as block trades:
Block trades are divided into two types: individual trades and paired trades. The price range for block trades is the same as the price fluctuation limits for regular trading in Taiwan. Individual trades are matched based on the declared prices and the chronological order of submission, while paired trades involve specific parties matching at designated prices and quantities. Additionally, the deadline for placing and matching block trades is 5:00 PM. To prevent significant intraday price fluctuations, block trades are typically executed after market hours.
This article uses the Windows 11 OS and VScode as the editor.
Obtaining insider declaration transfer data is imported from the directors and supervisors holding transfer database. The methods of transfers include various types such as regular trade, arranged with specific people, trust, gift, block trades, and trade the price after the plate. We precisely extract data from block trades.
import tejapi
import pandas as pd
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from datetime import datetime, timedelta
from dateutil.relativedelta import relativedelta
tejapi.ApiConfig.api_key = "Your Key"
tejapi.ApiConfig.api_base = "https://api.tej.com.tw"
tejapi.ApiConfig.ignoretz = True
# Retrieve insider declaration transfer data and filter out information related to block trades.
data_trans = tejapi.get('TWN/ATRANS',
# Securities code, declaration date, shareholder identity, transfer method, number of shares declared for transfer
opts = {'columns':['coid', 'mdate', 'clnm_e', 'tpnm_e', 'tns2']},
paginate = True)Next, we establish the total issued shares function, with data imported from the database containing the number of directors holding. In addition, the stock price function is imported from the database containing TSE/OTC unadjusted prices (daily).
Considering that the total issued shares of a company may fluctuate at different points in time, set the company code and declaration date as variables to input into the total issued shares function. This will provide the total issued shares at the corresponding time point and calculate the percentage of shares declared for transfer (%).
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Synthesizing the above data and related news, this article summarizes the following four results, taking 3036, 6452, 3017, 2311, 2597, and 2723 as examples：
We want to understand the changes in stock prices and returns after each stock declaration transfer. By calculating the next business day after the declaration date, we aim to observe whether the movement is positive or negative or if the stock price remains unaffected.
Calculate the number of daily return rates, categorizing them as positive if greater than 0, negative if less than 0, and zero if equal to 0.
After internal stock transfers, a pie chart depicting the changes in stock market returns on the next business day is generated. There are 488 records, with negative changes accounting for 49.2%, positive changes at 42.8%, and no change at approximately 8%.
The chart above represents the proportion of change rates in the closing prices on the next business day after the declaration date. It is evident that negative changes still dominate, accounting for nearly 50%. We can intuitively observe that investors are more inclined to withdraw funds when facing uncertainty. However, positive or unchanged movements are also possible if a company demonstrates strong fundamental qualities or if block trades do not result in share dilution.
From the data perspective, the impact of block trades on stock prices is worth paying attention to. We observe that after internal stock transfers, the short-term price changes mainly exhibit a negative trend, indicating that the market has a certain level of concern and uncertainty regarding such transactions.
However, block trades do not necessarily hurt stock prices. The analysis above shows that shareholders may sell their stocks due to personal financial planning or trust arrangements, which do not result in changes to the company’s share structure. Additionally, if a company’s financial performance is robust, or if the block trade is merely a reallocation of ownership, the stock price will not decline. Nevertheless, investors should closely monitor the company’s explanations regarding block trades to understand the purpose behind the stock transfer. Suppose a company fails to clearly explain the reasons for the transfer, coupled with poor operational performance or a general bearish sentiment in the market leading to consecutive price declines. Investors may need to consider reducing their holdings or selling their stocks.
This implementation analyzes insider transfers using the TEJ API. Using the data provided by TEJ API, we can implement various types of analysis as far as we can imagine. This approach saves time and effort and furnishes investors with a more diversified evaluation method. Explore more related solutions in TEJ Databank . Construct your trading strategy with a high-quality database.
Reminder: The strategy is only for reference and does not represent recommendations or advice on any financial products and investments.
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