STAT3622 Data Visualization (Lecture 10)

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# STAT3622 Data Visualization (Lecture 10)
## FinTech Case Studies with DataViz Skills
### <br>Dr. Aijun Zhang<br> The University of Hong Kong
### 14 April 2020

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# What's covered in this lecture?

- Applied AI in Banking and Finance
  
- Case 1: Financial Index Analysis
  
  - Review of AIEQ Case
  
  - Hang Seng Index

- Case 2: Financial Anomoly Detection

- Outlier Peeling Algorithm
  
  - Radial Chart Visualization
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# Applied AI in Banking and Finance

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# Applied AI in FinTech and Financial Services

There are multiple Financial Technology (FinTech) and Financial Service areas where Artificial Intelligence (AI) and Machine Learning (ML) can be applied, including

- Credit risk management (Credit scoring, Loan acquisition, Default forecasting, P2P lending)

- Investment analysis (Trading strategy, Portfolio optimization, Money management)

- Cryptocurrency (BitCoin, Crypto index)

- Fraud detection and anti-money laundering

- Customer service (Automated customer support via chatbots, Customer segmentatoin)

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# Two FinTech Case Studies

In this lecture, we discuss two FinTech case studies with the equipped DataViz skills:

- Case 1: Financial Index Analysis
  
  - Review of AIEQ Case 
  
  - Hang Seng Index

- Case 2: Finanial Anomoly Detection

- Outlier Peeling Algorithm
  
  - Radial Chart Visualization

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# Case 1: Financial Index Analysis

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# Investment Stratgies

- Active investing

- The market is not perfectly efficient 
  
  - Aims to beat the market and take advantage of short-term price fluctuations

- Passive investing

- The market is efficient in the long run 
  
  - Buy-and-hold philosophy 
  
  - Index tracking strategy, e.g. ETF

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# Review of AIEQ Case

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</div>

- Click [here](http://www.statsoft.org/wp-content/uploads/2017/09/Lecture12_AIEQ.html) for the reviewed case study, including AIEQ Performance, Holdings Analysis and Portfolio Analysis

---
# AIEQ Performance To Date

---
# New Case Study: Hang Seng Index

- Query data from Yahoo!Finance

- Interactive time series display by **R:dygraphs** ([Online Tutorial](https://rstudio.github.io/dygraphs/index.html))

```r
library(quantmod)
library(xts)
library(dygraphs)
library(htmltools)
date0 = "2000-01-01"
dummy = getSymbols("^HSI", src="yahoo", from = date0)
div(dygraph(HSI$HSI.Close, main = "Hang Seng Index since 2000") %>%
      dyRangeSelector(dateWindow = c("2010-01-01", "2018-12-31")) %>%
      dyOptions(colors = c("red")), 
    align="center")
```

---

<div align="center">
<div id="htmlwidget-579f056e745159c5f15e" style="width:960px;height:500px;" class="dygraphs html-widget"></div>
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</div>

---
# Hang Seng Index Constituents

- HSI historical data can be obtained from Bloomberg Terminal

- We collected daily weights of HSI constituents since 2016-01-01

---
# Hang Seng Index Constituents

- X.Shares <- X.Weight \* HSI.Close \* HSI.Divisor / X.Close

- Data Preprocessing to convert data into space-time format

```r
DataX = read.csv("DataHSI.csv")
DataX$Date2 = as.factor(DataX$Date)
tmp = DataX[!duplicated(DataX$Ticker, fromLast = T),]
idx = order(tmp$Date, tmp$Weight, decreasing = T)
ListTicker=tmp$Ticker[idx]
n = length(ListTicker) 
ListDate = as.character(unique(DataX$Date))
MatWeight <- MatShares <- matrix(NA, length(ListDate), n)
for (j in 1:n){
  TmpX = DataX[DataX$Ticker==ListTicker[j], c("Date2", "Weight", "Shares")]
  MatWeight[as.numeric(TmpX$Date2),j] = TmpX$Weight
  MatShares[as.numeric(TmpX$Date2),j] = TmpX$Shares
}
colnames(MatWeight) <- colnames(MatShares) <- ListTicker
MatWeight = xts(MatWeight, order.by=as.Date(ListDate, "%Y%m%d"))
MatShares = xts(MatShares, order.by=as.Date(ListDate, "%Y%m%d"))
```

---
# HSI Constituent Weights and Shares

```r
par(mfrow=c(1,2), mar=rep(2,4))
image(as.matrix(MatWeight), ylim=c(1,0), col = heat.colors(10), xaxt='n', yaxt='n', main="Weights")
image(as.matrix(MatShares), ylim=c(1,0), col = terrain.colors(10), xaxt='n', yaxt='n', main="Shares")
```

---

```r
div(dygraph(MatWeight, main = "HSI Constituent Weights",height=450) %>%
      dyRangeSelector(dateWindow = c("2016-01-01", "2018-12-31")) %>%
      dyLegend(show = "never"), align="center")
```

<div align="center">
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---

```r
TmpX = MatShares[,1:4]; TmpX = TmpX/rep(1, dim(TmpX)[1])%*%t(colMeans(TmpX, na.rm=T))
div(dygraph(TmpX, main = "HSI Constituent Shares", ylab="Normalized shares",height=450) %>%
      dyRangeSelector(dateWindow = c("2016-01-01", "2018-12-31")) %>%
      dyOptions(colors = c("red", "blue", "green", "cyan"), strokeWidth=2), align="center")
```

<div align="center">
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</div>

---
# HSI Constituent Rebalancing

```r
TmpX = MatShares[,"700.HK"]
div(dygraph(TmpX, main = "HSI Constituent Shares: 700.HK", height=360) %>%
      dyRangeSelector(dateWindow = c("2016-01-01", "2018-12-31")) %>%
      dyOptions(colors = "green"), align="center")
```

<div align="center">
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</div>

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# Question of interest:

### The AIEQ that tracks S&P500 is based on a semi-passive strategy, can we find its equivalent for Hang Seng index?

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# Case Study 2: Financial Anomoly Detection

Link to [Black September 2008](../Lecture10_BlackSep2008/index.html)

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# Thank you!

Q&A or Email ajzhang@umich.edu。