This document discusses new temporal models for forecasting weekly, monthly, bimonthly, quarterly, and half-yearly stock index data. It presents models built for the China 300 and Shanghai Composite indices using high, low, open, and close data from 2001 to 2015. The models show very close fit to the observed temporal distribution of the stock indices, with a goodness of fit of 99.99%. Forecasts are provided for upcoming periods of the China 300 and Shanghai Composite, and the author concludes the temporal modelling and forecasting methods can provide useful quantitative tools for stock trading.

1. New Temporal (Weekly, Monthly,
Bimonthly, Quarterly and Half-
yearly) Stock Index Modelling and
Forecasting in the STOCK Market
Shiquan REN, PhD
ctusren@outlook.com
https://au.linkedin.com/in/ctusren
June 23, 2015

2. Contents
•Background
•Temporal (Weekly, Monthly, Bimonthly,
Quarterly and Half-yearly) Data
•Temporal (Weekly, Monthly, Bimonthly,
Quarterly and Half-yearly) Modelling
•Forecasting/Prediction
•Conclusion

3. Background
•There are so many temporal data - monthly
and weekly (high/low) data in the stock
market
•Objective: I try to build and develop a series
of temporal models to forecast the half-
yearly, quarterly, bimonthly, monthly and
weekly (high/low) data distribution based
on the relationship of temporal data such as
high, low, open and close data

4. High, Low, Open and Close Data
•Weekly China 300 Index
• CHINA300 (30/12/2001 – 19/06/2015)
•Monthly Stock Index
• CHINA300 (2002/01 – 2015/05)
• SHCOMP (1994/01 – 2015/05)
•Bimonthly China 300 Index
• CHINA300 (2002/01 – 2015/04)
•Quarterly Shanghai Composite Index
• SHCOMP (1994/01 – 2015/03)
•Half-yearly Shanghai Composite Index
• SHCOMP (1994/01 – 2014/12)

5. New Temporal Modelling
•Weekly, Monthly, Quarterly and
Half-yearly Modelling of
High/Low Stock Index
•My own models and algorithms

6. Monthly Modelling of High SHCOMP Index
The goodness of fit between the observed and modelled data is 99.99%

7. Weekly Modelling of Low CHINA300 Index
The goodness of fit between the observed and modelled data is 99.99%

8. Weekly, Monthly and Bi-monthly Forecasting of
High/Low CHINA300 Index
model quantile Weekly Monthly Bimonthly
2015/06/22~26 2015/06 2015/05~06
high upper 5014 5643 5758
high 80% 4937 5429 5721
high 61.8% 4922 5403 5611
high median 4891 5382 5400
high 38.2% 4838 5362 5190
high 20% 4780 5315 5072
high lower 4691 5251 5024
low upper 4499 4656 4768
low 80% 4311 4428 4654
low 61.8% 4253 4410 4509
low median 4208 4378 4475
low 38.2% 4183 4347 4441
low 20% 4143 4331 4422
low lower 4127 4294 4158
The observed
highest index
in
2015/05~06
was 5395 on
8/06/2015;
the observed
lowest index
in 2015/06
was 4466 on
23/06/2015;
the observed
lowest index
in 2015/05
was 4451 on
8/05/2015

9. Monthly, Quarterly and Half-yearly Forecasting
of High/Low SHCOMP Index
model quantile Monthly Quarterly Half-yearly
2015/06 2015/04~06 2015/01~06
high upper 5508 5074 5181
high 80% 5162 4644 4282
high 61.8% 5114 4593 3785
high median 5072 4469 3229
high 38.2% 5030 4345 3229
high 20% 5011 4294 3229
high lower 4943 4211 3229
low upper 4418 3745 2745
low 80% 4388 3369 2669
low 61.8% 4352 3330 2650
low median 4245 3234 2626
low 38.2% 4167 3139 2601
low 20% 4117 3071 2591
low lower 4099 2863 2562
The observed
highest index
in 2015/01~06
was 5168 on
12/06/2015;
the observed
lowest index
in 2015/06
was 4260 on
23/06/2015;
the observed
lowest index
in 2015/04~06
was 3740 on
1/04/2015

10. Conclusion
•My new temporal modelling is very close to
the observed data, because the goodness of
fit between the observed and modelled
temporal high/low data is 99.99%
•My own forecasting/prediction of temporal
(weekly, monthly, bimonthly, quarterly and
half-yearly) high/low stock index is useful
and powerful methods for quantitative
trading in the STOCK market.