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Impact of Internet Stock News on Stock Markets Based on Neural Networks

Impact of Internet Stock News on Stock Markets Based on Neural Networks. Advances in neural networks - ISNN 2005 Presented by Joshua M. Y. Chan in COMP630P 2009 – HKUST. Outline. Objective of Paper Background Experiment Details Results Comments Possible Extensions. Objective of Paper.

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Impact of Internet Stock News on Stock Markets Based on Neural Networks

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  1. Impact of Internet Stock News on Stock Markets Based on Neural Networks Advances in neural networks - ISNN 2005 Presented by Joshua M. Y. Chan in COMP630P 2009 – HKUST

  2. Outline • Objective of Paper • Background • Experiment Details • Results • Comments • Possible Extensions

  3. Objective of Paper • Stock information has significant impact on stock price • Newspaper • Television • Financial reports and more… • Nowadays, internet becomes one of the most important media and the main source of stock information

  4. Objective of Paper • Study the correlation between internet stock news (ISN) and stock returns • Empirically explore the relations between using neural networks • ISN VS stock returns • ISN volume VS change of stock price • Provide experimental evidence on the possible relations • Discover the microstructure of stock market

  5. Background • Focus on webpages in the form of XML which contains • Contents of information • Style • And XML tags (Header) • In the XML tags provide useful information about the articles • For example, • <IntensityImpacted>0.5</IntensityImpacted> • It shows that the intensity impacted by the ISN on the markets is calibrated as 0.5

  6. Background • The author assumed that in the next generation of webpages, all the ISN published by the web servers of large Internet media will be incorporated in some tags, e.g., • subjective or predictive intensity of the ISN, • the range of investors that the news impacts, etc, • Tags may be invisible to webpage readers, but visible to the computer programs. • Therefore, relationship between headers and stock returns can be mined by neural networks.

  7. Background • Assume that five tags are present in each ISN • DurationImpacted [0..1] - duration that the news impacts • IntensityImpacted [-1..1] - intensity of the news • CirculationRange [0..1] - range of investors that the news impacts • StockMarketStatus [-1..1] - bear or bull market • IndustryStatus [-1..1] - status in the corresponding industry • They are subjectively provided by contributors based on their expertise and prediction to the impact of news

  8. Experiment Details • ISN collected from various financial news web servers • zgjrw.com, www.sic-cfi.com, www.cfi.net.cn, www.cnlist.com, www.chinabond.com.cn, www.cnfund.cn/jjcs.asp, finance.sina.com.cn, www.sse.com.cn, www.szse.cn, www.hexun.com, www.cs.com.cn, quote.stock.163.com/stock/index, www.pa18.com, www.cnfxj.com, www.boyaxun.com, www.stock2000.net • 236 pieces of news collected and divided into 2 sets • training set (182 news) and testing set (54 news)

  9. Experiment Details • Consider the following two numbers • return on the day when the news occurs r0, and • return in the next day after the news occurs r1. • The stock return r = max{r0, r1} • r=r0, the effect of the ISN may be reflected immediately or even before it is announced • r=r1 , the impact of the ISN has certain psychological delay in investors

  10. Experiment Result • Using neural network (5-H-1) to explore the mapping between <DurationImpacted>, <IntensityImpacted>, <Circulation Range>, <StockMarketStatus>, <IndustryStatus> and stock returns • Only the sign of the stock return r is considered, i.e., sign(r) • H=43 is the optimal value derived from the experiment • Results • Among the 54 testing data, the trained neural network can correctly predict 33 signs of returns (61%) • 24 cases with relatively larger intensities, the correct rate is 17/24 = 71%. • 3 cases with very large intensities, the correct rate is 3/3 = 100%.

  11. Experimental Results • Conclusion on results • the signs of stock returns, or the directions of stock price movements, on the day or the following day of the corresponding ISN, tend to be predictive based on the neural networks, and • the news with larger intensity is more likely to predict the market movement than the average.

  12. ISN Volume Vs Stock Price • Second part of the experiment study the relation between the daily ISN volume and stock price changes • Verify that a “significant” increase in volume of ISN implies a “significant” increase in change of stock price

  13. ISN Volume Vs Stock Price • Definitions of ISN volume • company i, i = 1, …, I, where I is the number of companies • volume of ISN on day k, denoted by vi(k), is defined as the number of pieces of ISN on the sub-website of company i, k = -K, … , -1, 0, where K is the observation period, (e.g., K = 60) • Mean μi and standard deviation σi of vi(k) • If vi on day k satisfies μi+2σi ≤ vi , a significant increase in the volumes of ISN occurs for company i on day k • Magnitude of the volume of ISN Θi = max{(vi- μi)/σi, 0}

  14. ISN Volume Vs Stock Price • Definitions of stock price • pi(k) is the stock price of company i on day k • Stock price change on day k is given by Δpi(k) = pi(k) – pi(k-1) • Mean and standard deviation are given by λi and τi • If the stock price change Δpi(k) on day k satisfies λi+2τi≤|Δpi(k)|, a significant increase in the stock price occurs

  15. Experimental Results • Input pattern • Xi(k) = (θi(k+1), θi(k), θi(k-1), … , θi(k-L+1), |pi(k)|, |pi(k-1)|, …, |pi(k-L+1)|)τ ∈ [0,1](2L+1)X1 • Output pattern • |pi(k+1)| ∈ [0,1]1×1 • L=9 (#old news)

  16. Experimental Results Predicted Change Training Data Testing Data There exists some relationship between the volumes of ISN and stock price movements. Overnight stock message posting volume is predictive to the change of stock returns Testing errors are less than 28%.

  17. Conclusion contents of the ISN are related to the stock returns based on the XML protocols volume of the ISN is associated with the stock price movements ISN is becoming a new indicator for stock price movements

  18. Comments • Idea is not technically sound, experiment is too simple, problem is not well-studied, results are not new or inspiring… and more (typos, grammatical mistakes) • Drawbacks • headers are subjective and so to some extent the results would depend on how they are rated manually. • News are not reliable. (manipulation? Diverse?) • Important news duplicates each other. • The results do not comply with the efficient market hypothesis

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