Difference between revisions of "Group 4 Report"
| Line 28: | Line 28: | ||
|} | |} | ||
<br/> | <br/> | ||
| + | |||
| + | =Analytics= | ||
| + | ==1. Time Series== | ||
| + | |||
| + | A simple line plot of different daily close price against time shows price changes over a period of 5 years. From first observation (chart below), it is expected that the price volatility from 2016 onwards will be high. Further discussion on this volatility is set out at a later part of this research. | ||
| + | From this chart, we can observe massive price movements in 2014 and 2017. The trend is close to an exponential curve see in 2017. | ||
| + | |||
| + | [Attach image] | ||
| + | |||
| + | Zooming into the period between 1 September 2017 and 15 September 2017 during which the price of bitcoin dropped from about $4,900 to about $3,200 in a matter of 10 business days, the following is chart is what we observe: | ||
| + | |||
| + | [Attach image] | ||
| + | |||
| + | |||
| + | The candle stick chart was helpful to highlight large drops in prices in minute blocks. Further investigation reveals that during this period, various negative news was released. The following is a list of them : | ||
| + | |||
| + | # 14th Sept: Elvira Nabiullina, Governor Bank of Russia: ''“China doesn’t recognize cryptocurrency as payment and forbids ICOs … Our views are absolutely similar. In our view, it's a sort of a financial pyramid that may collapse at any moment.”'' | ||
| + | # 12th Sept: Jamie Dimon, CEO of JPMorgan Chase: ''“It’s a fraud.” If a JPMorgan trader began trading in bitcoin, he said “I'd fire them in a second. For two reasons: It’s against our rules, and they’re stupid. And both are dangerous.”'' | ||
| + | # 8th Sept: Howard Marks, Co-chairman and co-founder Oaktree Capital Group: ''“So my initial bottom line is that I see no reason why bitcoin can’t be a currency … But I still don’t feel like putting my money into it, because I consider it a speculative bubble. I'm willing to be proved wrong.”'' | ||
| + | # 5th Sept: Robert Shiller, Nobel Prize winning professor of economics, Yale University: ''“The best example [of a speculative bubble] right now is bitcoin”'' | ||
| + | |||
| + | ''(Source:Bloomberg, Bitcoin bulls and bears <https://www.bloomberg.com/features/bitcoin-bulls-bears/>'' | ||
| + | |||
| + | |||
| + | The price seems to be reactive to news released which may be a sign of speculative investors looking for gains in bitcoin investment. Another tell tale sign that investor may be pouring money into bitcoin was the historic low volatility levels in the financial markets . Low volatility in financial markets means lower money making opportunity, thus, making bitcoin a potential alternative. | ||
| + | |||
| + | |||
| + | ==2. Cyclical Effects== | ||
| + | |||
| + | The technique applied to account for seasonality is through the use of Auto Correlation Function (ACF). How this works is as follow: | ||
| + | * the data is replicated and shifted down the time series known as lags; | ||
| + | * correlation is then computed between the lag against the original data points; and | ||
| + | * finally, it is plotted on an ACF graph. | ||
| + | |||
| + | The sample table below shows the closing price and its respective lags in R: | ||
| + | |||
| + | [Attach Image] | ||
| + | |||
| + | The purpose of performing the lags and calculating the correlation is to identify whether there is any repeating trends in the underlying data as time passes. If trends do change, we will observe a drop in correlation and vice versa. | ||
| + | |||
| + | We will analyse this topic in four parts, being: | ||
| + | # entire data point and observe for seasonality up to lag 90; | ||
| + | # data between 2014 and 2016 with 360 day lag; | ||
| + | # the year 2017; and | ||
| + | # specific months in 2017. | ||
| + | |||
| + | ===2.1 Entire data point and observe for seasonality up to lag 90=== | ||
| + | |||
| + | [Attach Image] | ||
| + | |||
| + | There is strong correlation even up to 90 day lag. This is expected because we have a massive spike in price between 2016 and 2017. The correlation value would have been heavily influenced by the bitcoin prices towards the tail end. Thus, unless we drag the lag into extreme number of days, we are unlikely to see any meaningful trend from the above. | ||
| + | |||
| + | |||
| + | === 2.2 Data between 2014 and 2016 with 180 day lag=== | ||
| + | |||
| + | Next, we explore auto-correlation in the period between 2014 and 2016. This period was selected because there was some price activity towards end of 2013 and right before the massive price increase in 2017. We dragged the lag to the extreme of 180 days (half a year). | ||
| + | |||
| + | [Attach Image] | ||
| + | |||
| + | |||
| + | The strong correlation gradually tapered off towards the 6 months. There is no strong indication of seasonality seen from this chart. | ||
| + | |||
| + | |||
| + | === 2.3 Year 2017=== | ||
| + | |||
| + | [Attach Image] | ||
| + | |||
| + | The above encompasses all data points in the year 2017. No seasonality is observed up to for 90 day lags. This chart is likely skewed due to the 700% increase in bitcoin value from the start of the year. Thus, the upward trend is apparent. | ||
| + | |||
| + | |||
| + | ===2.4 Specific Months in 2017=== | ||
| + | |||
| + | [Attach Image] | ||
| + | |||
| + | The above is charted based on bitcoin price between August 2017 and October 2017. Trends begin to change at 10 day period intervals, which show some form of cyclical effect. This trend is not apparent in other periods of 2017. The fluctuations may be purely coincidental and could be a consequence of a series of market news released during this period which drove the prices up and down at specific intervals. | ||
| + | |||
| + | |||
| + | ===2.5 Conclusion for this sub-segment=== | ||
| + | |||
| + | Due to the high volatility in recent prices and a strong tendency upwards, a longer period auto-correlation does not reveal much information apart from what the price chart has already done. However, analysis at specific shorter period, such as the three month period between August 2017 and October 20 17, reveals some form of cyclical effect (10 day cycle). | ||
Revision as of 12:26, 30 November 2017
Group 4 Project - A Tale of Bitcoin
|
|
|
|
|
|
|
|
Contents
Analytics
1. Time Series
A simple line plot of different daily close price against time shows price changes over a period of 5 years. From first observation (chart below), it is expected that the price volatility from 2016 onwards will be high. Further discussion on this volatility is set out at a later part of this research. From this chart, we can observe massive price movements in 2014 and 2017. The trend is close to an exponential curve see in 2017.
[Attach image]
Zooming into the period between 1 September 2017 and 15 September 2017 during which the price of bitcoin dropped from about $4,900 to about $3,200 in a matter of 10 business days, the following is chart is what we observe:
[Attach image]
The candle stick chart was helpful to highlight large drops in prices in minute blocks. Further investigation reveals that during this period, various negative news was released. The following is a list of them :
- 14th Sept: Elvira Nabiullina, Governor Bank of Russia: “China doesn’t recognize cryptocurrency as payment and forbids ICOs … Our views are absolutely similar. In our view, it's a sort of a financial pyramid that may collapse at any moment.”
- 12th Sept: Jamie Dimon, CEO of JPMorgan Chase: “It’s a fraud.” If a JPMorgan trader began trading in bitcoin, he said “I'd fire them in a second. For two reasons: It’s against our rules, and they’re stupid. And both are dangerous.”
- 8th Sept: Howard Marks, Co-chairman and co-founder Oaktree Capital Group: “So my initial bottom line is that I see no reason why bitcoin can’t be a currency … But I still don’t feel like putting my money into it, because I consider it a speculative bubble. I'm willing to be proved wrong.”
- 5th Sept: Robert Shiller, Nobel Prize winning professor of economics, Yale University: “The best example [of a speculative bubble] right now is bitcoin”
(Source:Bloomberg, Bitcoin bulls and bears <https://www.bloomberg.com/features/bitcoin-bulls-bears/>
The price seems to be reactive to news released which may be a sign of speculative investors looking for gains in bitcoin investment. Another tell tale sign that investor may be pouring money into bitcoin was the historic low volatility levels in the financial markets . Low volatility in financial markets means lower money making opportunity, thus, making bitcoin a potential alternative.
2. Cyclical Effects
The technique applied to account for seasonality is through the use of Auto Correlation Function (ACF). How this works is as follow:
- the data is replicated and shifted down the time series known as lags;
- correlation is then computed between the lag against the original data points; and
- finally, it is plotted on an ACF graph.
The sample table below shows the closing price and its respective lags in R:
[Attach Image]
The purpose of performing the lags and calculating the correlation is to identify whether there is any repeating trends in the underlying data as time passes. If trends do change, we will observe a drop in correlation and vice versa.
We will analyse this topic in four parts, being:
- entire data point and observe for seasonality up to lag 90;
- data between 2014 and 2016 with 360 day lag;
- the year 2017; and
- specific months in 2017.
2.1 Entire data point and observe for seasonality up to lag 90
[Attach Image]
There is strong correlation even up to 90 day lag. This is expected because we have a massive spike in price between 2016 and 2017. The correlation value would have been heavily influenced by the bitcoin prices towards the tail end. Thus, unless we drag the lag into extreme number of days, we are unlikely to see any meaningful trend from the above.
2.2 Data between 2014 and 2016 with 180 day lag
Next, we explore auto-correlation in the period between 2014 and 2016. This period was selected because there was some price activity towards end of 2013 and right before the massive price increase in 2017. We dragged the lag to the extreme of 180 days (half a year).
[Attach Image]
The strong correlation gradually tapered off towards the 6 months. There is no strong indication of seasonality seen from this chart.
2.3 Year 2017
[Attach Image]
The above encompasses all data points in the year 2017. No seasonality is observed up to for 90 day lags. This chart is likely skewed due to the 700% increase in bitcoin value from the start of the year. Thus, the upward trend is apparent.
2.4 Specific Months in 2017
[Attach Image]
The above is charted based on bitcoin price between August 2017 and October 2017. Trends begin to change at 10 day period intervals, which show some form of cyclical effect. This trend is not apparent in other periods of 2017. The fluctuations may be purely coincidental and could be a consequence of a series of market news released during this period which drove the prices up and down at specific intervals.
2.5 Conclusion for this sub-segment
Due to the high volatility in recent prices and a strong tendency upwards, a longer period auto-correlation does not reveal much information apart from what the price chart has already done. However, analysis at specific shorter period, such as the three month period between August 2017 and October 20 17, reveals some form of cyclical effect (10 day cycle).
