Impact of Internet Stock Impact of Internet Stock News on Stock Markets News on Stock Markets

Based on Neural NetworksBased on Neural Networks

Advances in neural networks - ISNN 2005Advances in neural networks - ISNN 2005  

Presented by Joshua M. Y. ChanPresented by Joshua M. Y. Chan

in COMP630P 2009 in COMP630P 2009 –– HKUST HKUST

OutlineOutline

Objective of PaperObjective of Paper BackgroundBackground Experiment DetailsExperiment Details ResultsResults CommentsComments Possible ExtensionsPossible Extensions

Objective of PaperObjective of Paper

Stock information has significant Stock information has significant impact on stock priceimpact on stock price• Newspaper Newspaper • TelevisionTelevision• Financial reports and more…Financial reports and more…

Nowadays, internet becomes one of Nowadays, internet becomes one of the most important media and the the most important media and the main source of stock informationmain source of stock information

Objective of PaperObjective of Paper

Study the correlation between Study the correlation between internet internet stock news stock news (ISN) and stock returns(ISN) and stock returns• Empirically explore the relations between Empirically explore the relations between

using neural networksusing neural networks• ISN VS stock returnsISN VS stock returns• ISN volume VS change of stock priceISN volume VS change of stock price

Provide experimental evidence on the Provide experimental evidence on the possible relationspossible relations

Discover the microstructure of stock marketDiscover the microstructure of stock market

BackgroundBackground

Focus on webpages in the form of XML which Focus on webpages in the form of XML which containscontains• Contents of informationContents of information• StyleStyle• And And XML tags (Header)XML tags (Header)

In the XML tags provide useful information about In the XML tags provide useful information about the articlesthe articles

For example,For example,• <IntensityImpacted>0.5</IntensityImpacted><IntensityImpacted>0.5</IntensityImpacted>• It shows that the intensity impacted by the ISN It shows that the intensity impacted by the ISN

on the markets is calibrated as 0.5on the markets is calibrated as 0.5

BackgroundBackground

The author assumed that in the next generation The author assumed that in the next generation of webpages, all the ISN published by the web of webpages, all the ISN published by the web servers of large Internet media will be servers of large Internet media will be incorporated in some tags, e.g., incorporated in some tags, e.g., • subjective or predictive intensity of the ISN, subjective or predictive intensity of the ISN, • the range of investors that the news impacts, the range of investors that the news impacts,

etc, etc, Tags may be invisible to webpage readers, but Tags may be invisible to webpage readers, but

visible to the computer programs.visible to the computer programs. Therefore, relationship between headers and Therefore, relationship between headers and

stock returns can be mined by neural networks.stock returns can be mined by neural networks.

BackgroundBackground Assume that five tags are present in each ISNAssume that five tags are present in each ISN

• DurationImpacted [0..1] - duration that the news DurationImpacted [0..1] - duration that the news impactsimpacts

• IntensityImpacted [-1..1] - intensity of the newsIntensityImpacted [-1..1] - intensity of the news• CirculationRange [0..1] - range of investors that the CirculationRange [0..1] - range of investors that the

news impactsnews impacts• StockMarketStatus [-1..1] - bear or bull marketStockMarketStatus [-1..1] - bear or bull market• IndustryStatus [-1..1] - status in the corresponding IndustryStatus [-1..1] - status in the corresponding

industryindustry They are subjectively provided by contributors based on They are subjectively provided by contributors based on

their expertise and prediction to the impact of newstheir expertise and prediction to the impact of news

Experiment DetailsExperiment Details

ISN collected from various financial news web ISN collected from various financial news web serversservers• zgjrw.com, www.sic-cfi.com, www.cfi.net.cn, zgjrw.com, www.sic-cfi.com, www.cfi.net.cn,

www.cnlist.com, www.cnlist.com, www.chinabond.com.cn, , www.cnfund.cn/jjcs.asp, finance.sina.com.cn, www.cnfund.cn/jjcs.asp, finance.sina.com.cn, www.sse.com.cn, www.sse.com.cn, www.szse.cn, www.hexun.com, , www.hexun.com, www.cs.com.cn, quote.stock.163.com/stock/index, , quote.stock.163.com/stock/index, www.pa18.com, www.cnfxj.com, www.boyaxun.com, , www.cnfxj.com, www.boyaxun.com, www.stock2000.net

236 pieces of news collected and divided into 2 236 pieces of news collected and divided into 2 setssets

training set (182 news) and testing set (54 news)training set (182 news) and testing set (54 news)

Experiment DetailsExperiment Details

Consider the following two numbersConsider the following two numbers

• return on the day when the news occurs return on the day when the news occurs rr0,0, and and

• return in the next day after the news occurs return in the next day after the news occurs rr11..

The stock return r = max{rThe stock return r = max{r00, r, r11}}

• r=rr=r0, 0, the effect of the ISN may be reflected the effect of the ISN may be reflected immediately or even before it is announcedimmediately or even before it is announced

• r=rr=r1 , 1 , the impact of the ISN has certain the impact of the ISN has certain psychological delay in investorspsychological delay in investors

Experiment ResultExperiment Result Using neural network (5-H-1) to explore the mapping Using neural network (5-H-1) to explore the mapping

between <DurationImpacted>, <IntensityImpacted>, between <DurationImpacted>, <IntensityImpacted>, <Circulation Range>, <StockMarketStatus>, <Circulation Range>, <StockMarketStatus>, <IndustryStatus> and stock returns<IndustryStatus> and stock returns

Only the sign of the stock return r is considered, i.e., sign(r)Only the sign of the stock return r is considered, i.e., sign(r) H=43 is the optimal value derived from the experimentH=43 is the optimal value derived from the experiment ResultsResults

• Among the 54 testing data, the trained neural network can correctly Among the 54 testing data, the trained neural network can correctly predict 33 signs of returns (61%)predict 33 signs of returns (61%)

• 24 cases with relatively larger intensities, the correct rate is 17/24 = 24 cases with relatively larger intensities, the correct rate is 17/24 = 71%.71%.

• 3 cases with very large intensities, the correct rate is 3/3 = 100%.3 cases with very large intensities, the correct rate is 3/3 = 100%.

Experimental ResultsExperimental Results

Conclusion on resultsConclusion on results• the signs of stock returns, or the the signs of stock returns, or the

directions of stock price movements, on directions of stock price movements, on the day or the following day of the the day or the following day of the corresponding ISN, tend to be predictive corresponding ISN, tend to be predictive based on the neural networks, and based on the neural networks, and

• the news with larger intensity is more the news with larger intensity is more likely to predict the market movement likely to predict the market movement than the average.than the average.

ISN Volume Vs Stock PriceISN Volume Vs Stock Price

Second part of the experiment study Second part of the experiment study the relation between the daily ISN the relation between the daily ISN volume and stock price changesvolume and stock price changes

Verify that a “significant” increase in Verify that a “significant” increase in volume of ISN implies a “significant” volume of ISN implies a “significant” increase in change of stock price increase in change of stock price

ISN Volume Vs Stock PriceISN Volume Vs Stock Price Definitions of ISN volumeDefinitions of ISN volume

• company company i, i = 1, …, I, where I is the number i, i = 1, …, I, where I is the number of of companiescompanies

• volume of ISN on day volume of ISN on day k, denoted by vk, denoted by vii(k), is defined as (k), is defined as the number of the number of pieces of ISN on the sub-website of pieces of ISN on the sub-website of company company i, k = -K, … , -1, 0, where K is the observation i, k = -K, … , -1, 0, where K is the observation period, (e.g., period, (e.g., K = 60)K = 60)

• Mean Mean μμii and standard deviation and standard deviation σσi i of vof vii(k)(k)

• If If vvii on day k satisfies μ on day k satisfies μii+2σ+2σii ≤ v ≤ vii , a significant increase , a significant increase in the volumes in the volumes of ISN occurs for company of ISN occurs for company i on day ki on day k

• Magnitude of the volume of ISN Magnitude of the volume of ISN ΘΘi = max{(vi = max{(vii-- μ μii)/)/σσii, 0, 0}}

ISN Volume Vs Stock PriceISN Volume Vs Stock Price

Definitions of stock priceDefinitions of stock price• pi(k) is the stock price of company i on day pi(k) is the stock price of company i on day

kk• Stock price change on day k is given by Stock price change on day k is given by

ΔΔppii(k) = p(k) = pii(k) – p(k) – pii(k-1)(k-1)

• Mean and standard deviation are given by Mean and standard deviation are given by λλii and and ττii

• If the stock price change If the stock price change ΔΔppii(k) on day k (k) on day k satisfies satisfies λλii+2+2ττii≤|≤|ΔΔppii(k)(k)|,|, a significant a significant increase in the stock priceincrease in the stock price occurs occurs

Experimental ResultsExperimental Results

Input patternInput pattern• XXii(k) = ((k) = (θθii(k+1), (k+1), θθii(k), (k), θθii(k-1), … , (k-1), … , θθii(k-(k-

L+1), |pL+1), |pii(k)|, (k)|, ||ppii(k-1)|, …, |p(k-1)|, …, |pii(k-L+1)|)(k-L+1)|)τ τ ∈ ∈ [0,1[0,1]](2L+1)X1(2L+1)X1

Output patternOutput pattern• ||ppii(k+1)| ∈ [0,1](k+1)| ∈ [0,1]1×11×1

L=9 (#old news)L=9 (#old news)

Experimental ResultsExperimental Results

There exists some relationship between the volumes of ISN There exists some relationship between the volumes of ISN and stock price movements. and stock price movements.

Overnight stock message posting volume is predictive to Overnight stock message posting volume is predictive to the change of stock returns the change of stock returns

Testing errors are less than 28%.Testing errors are less than 28%.

Training Data

Testing Data

Predicted Change

ConclusionConclusion

contents of the ISN are related to the contents of the ISN are related to the stock returns based on the XML stock returns based on the XML protocolsprotocols

volume of the ISN is associated with volume of the ISN is associated with the stock price movementsthe stock price movements

ISN is becoming a new indicator for ISN is becoming a new indicator for stock price movementsstock price movements

CommentsComments

Idea is not technically sound, experiment is too Idea is not technically sound, experiment is too simple, problem is not well-studied, results are not simple, problem is not well-studied, results are not new or inspiring… and more (typos, grammatical new or inspiring… and more (typos, grammatical mistakes)mistakes)

DrawbacksDrawbacks• headers are subjective and so to some extent headers are subjective and so to some extent

the results would depend on how they are rated the results would depend on how they are rated manually.manually.

• News are not reliable. (manipulation? Diverse?)News are not reliable. (manipulation? Diverse?)• Important news duplicates each other.Important news duplicates each other.• The results do not comply with the efficient The results do not comply with the efficient

market hypothesismarket hypothesis