def processing_est_cash_dividend_average_dividend_payout_ratio(
stockid, year_stat, year_est):
print(format(stockid) + '...')
fs_dividendpolicy = class_fs_dividendpolicy(findbillion_database)
dividend_cash_true = fs_dividendpolicy.get_Dividend_Cash(stockid, year_est)
est_eps = class_est_eps(findbillion_database)
eps_est = est_eps.est_last_4q_eps_by_netincome_ratio(stockid, year_stat, 4)
est_yield = class_est_yield(findbillion_database)
est_dividend_cash = est_yield.est_cash_dividend_by_dividend_payout_ratio(
stockid, year_stat, eps_est)
if dividend_cash_true is not None and est_dividend_cash is not None:
if dividend_cash_true == 0:
err_percentage = None
else:
err_percentage = (est_dividend_cash -
dividend_cash_true) / dividend_cash_true * 100
else:
dividend_cash_true, est_dividend_cash, err_percentage = None, None, None\
return {
stockid: dividend_cash_true
}, {
stockid: est_dividend_cash
}, {
stockid: err_percentage
}
def process_return_average_per_for_est_cash_dividend_by_netincome_ratio(
stockid, year_est, year_stat, hold_year, expect_return, yield_buyin):
#fs_dividendpolicy = class_fs_dividendpolicy(findbillion_database)
est_eps = class_est_eps(findbillion_database)
eps_est_last_4q = est_eps.est_last_4q_eps_by_netincome_ratio(
stockid, year_est, 4)
est_yield = class_est_yield(findbillion_database)
est_dividend_cash = est_yield.est_cash_dividend_by_linear_regression_5y(
stockid, year_stat, eps_est_last_4q)
monthprice = class_monthprice(findbillion_database)
price_buyin = monthprice.get_PriceHigh(
stockid, year_est + 1,
1) # +1, estimate 2018 Q4 EPS, 2019/1 get revenues,
if est_dividend_cash is not None and price_buyin is not None:
est_dividend_cash_yield = est_dividend_cash / price_buyin
else:
est_dividend_cash_yield = None
if stockid == 1234:
debug_tmp = 1
PERatio = get_average_PERatio(stockid, year_est, 1, 3)
eps_est_last_4q = est_eps.est_last_4q_eps_by_netincome_ratio(
stockid, year_est, 4)
if eps_est_last_4q is not None and PERatio is not None:
price_target = eps_est_last_4q * PERatio
else:
price_target = 0
price_buyin = monthprice.get_PriceHigh(stockid, year_est + 1, 1)
if price_buyin is not None:
price_target_buyin_diff = (price_target - price_buyin) / price_buyin
else:
price_target_buyin_diff = None
if price_buyin is not None and price_target_buyin_diff is not None and price_target_buyin_diff>expect_return \
and est_dividend_cash_yield is not None and est_dividend_cash_yield>yield_buyin: # and PERatio<20
price_high_hold = get_high_price_hold(stockid, year_est + 1, 1,
hold_year)
price_target_high_diff = (price_high_hold -
price_target) / price_target
return_max = (price_high_hold - price_buyin) / price_buyin
else:
price_buyin = None
price_high_hold = None
price_target_high_diff = None
return_max = None
return {stockid: price_target}, \
{stockid: PERatio}, \
{stockid: price_buyin}, \
{stockid: price_target_buyin_diff}, \
{stockid: price_high_hold}, \
{stockid: price_target_high_diff}, \
{stockid: return_max}
def main():
findbillion_database = class_findbillion_database(dataset_path)
#
est_eps = class_est_eps(findbillion_database)
est_yield = class_est_yield(findbillion_database)
#-----------------------------------------
# ground truth of dividend_cash
fs_dividendpolicy = class_fs_dividendpolicy(findbillion_database)
dividend_cash = fs_dividendpolicy.get_Dividend_Cash(stockid, year_est)
#-----------------------------------------
# methed 1: average cash dividend
dividend_cash_avg = est_yield.get_average_5y_cash_dividend(
stockid, year_stat)
error_method1 = (dividend_cash_avg - dividend_cash) / dividend_cash * 100
print("Average cash dividend: ")
print("cash dividend (true): {:2.2f}".format(dividend_cash))
print("cash dividend (predict): {:2.2f}".format(dividend_cash_avg))
print("error of predict: {:2.2f}%".format(error_method1))
#--------------------------------------------------
# methed 2: average dividend payout ratio
print("----------------------------------------")
eps_est_last4q = est_eps.est_last_4q_eps_by_netincome_ratio(
stockid, year_est, 4)
est_dividend_cash = est_yield.est_cash_dividend_by_dividend_payout_ratio(
stockid, year_stat, eps_est_last4q)
error_method2 = (est_dividend_cash - dividend_cash) / dividend_cash * 100
print("Average cash dividend payout ratio: ")
print("cash dividend (true): {:2.2f}".format(dividend_cash))
print("cash dividend (predict): {:2.2f}".format(est_dividend_cash))
print("error of predict: {:2.2f}%".format(error_method2))
#--------------------------------------------------
# methed 3: estimate dividend payout ratio by linear regression
print("----------------------------------------")
eps_est = est_eps.est_last_4q_eps_by_netincome_ratio(stockid, year_est, 4)
est_dividend_cash = est_yield.est_cash_dividend_by_linear_regression_5y(
stockid, year_stat, eps_est)
error_method3 = (est_dividend_cash - dividend_cash) / dividend_cash * 100
print("Estimate cash dividend payout ratio by linear regression: ")
print("cash dividend (true): {:2.2f}".format(dividend_cash))
print("cash dividend (predict): {:2.2f}".format(est_dividend_cash))
print("error of predict: {:2.2f}%".format(error_method3))
def processing_return_for_est_cash_dividend_by_netincome_ratio_with_revenue_growth(stockid, year_stat, year_est, yield_buyin, hold_year):
print(format(stockid) + '...')
# cash dividend (ground truth)
fs_dividendpolicy = class_fs_dividendpolicy(findbillion_database)
dividend_cash_true = fs_dividendpolicy.get_Dividend_Cash(stockid, year_est)
# estimate eps
est_eps = class_est_eps(findbillion_database)
eps_est_last_4q = est_eps.est_last_4q_eps_by_netincome_ratio(stockid, year_est, 4)
# estimate cash dividend by linear_regression
est_yield = class_est_yield(findbillion_database)
est_dividend_cash = est_yield.est_cash_dividend_by_linear_regression_5y(stockid, year_stat, eps_est_last_4q)
# estimate revenue growth
check_year = 3
year_end = year_est
month_end = 12
year_start = year_end - check_year+1 # 2017/1~2019/12
month_start = 1
est_growth = class_est_growth(findbillion_database)
growth_rate = est_growth.get_revenue_growth(stockid, year_start, month_start, year_end, month_end, 0)
# buy price at next year/Jan
monthprice = class_monthprice(findbillion_database)
price_buyin = monthprice.get_PriceHigh(stockid, year_est+1, 1) # +1, estimate 2018 Q4 EPS, 2019/1 get revenues,
if est_dividend_cash is not None and price_buyin is not None:
est_dividend_cash_yield = est_dividend_cash/price_buyin
else:
est_dividend_cash_yield = None
# buy when estimate cash yield > {yield_buyin}
if price_buyin is not None and est_dividend_cash_yield is not None and est_dividend_cash_yield>yield_buyin and growth_rate is not None and growth_rate>0:
price_buyin_hold = monthprice.getPriceClose(stockid, year_est+1+hold_year, 1)
return_buyin = cal_retrun(stockid, price_buyin, price_buyin_hold, year_est, hold_year, fs_dividendpolicy)
else:
price_buyin = None
price_buyin_hold = None
return_buyin = None
return {stockid: dividend_cash_true}, \
{stockid: est_dividend_cash}, \
{stockid: est_dividend_cash_yield}, \
{stockid: growth_rate}, \
{stockid: price_buyin}, \
{stockid: price_buyin_hold}, \
{stockid: return_buyin}
def func_est_eps(stockid, year_est):
quarter_est = 4
financial_ratio = class_financial_ratio(findbillion_database)
est_eps = class_est_eps(findbillion_database)
eps_true = financial_ratio.get_eps_last_4q(stockid, year_est, quarter_est)
eps_pred = est_eps.est_last_4q_eps_by_netincome_ratio(
stockid, year_est, quarter_est)
if eps_true is None or eps_pred is None:
err_percentage = None
elif eps_true != 0:
err_percentage = (eps_pred - eps_true) / eps_true * 100
else:
err_percentage = None #(eps_pred - 0.001) / 0.001 * 100
return eps_true, eps_pred, err_percentage
def main():
year = 2019
quarter = 4
stockid = 2330
findbillion_database = class_findbillion_database(dataset_path)
financial_ratio = class_financial_ratio(findbillion_database)
est_eps = class_est_eps(findbillion_database)
eps_true = financial_ratio.get_eps_last_4q(stockid, year, quarter)
eps_pred = est_eps.est_last_4q_eps_by_netincome_ratio(
stockid, year, quarter)
err_percentage = (eps_pred - eps_true) / eps_true * 100
print("eps (true): {:2.2f}".format(eps_true))
print("eps (predict): {:2.2f}".format(eps_pred))
print("error of predict: {:2.2f}%".format(err_percentage))
def process_average_per(stockid, year_est, year_stat, hold_year,
expect_return):
fs_dividendpolicy = class_fs_dividendpolicy(findbillion_database)
est_eps = class_est_eps(findbillion_database)
monthprice = class_monthprice(findbillion_database)
if stockid == 1234:
debug_tmp = 1
PERatio = get_average_PERatio(stockid, year_est, 1, 3)
eps_est_last_4q = est_eps.est_last_4q_eps_by_netincome_ratio(
stockid, year_est, 4)
if eps_est_last_4q is not None and PERatio is not None:
price_target = eps_est_last_4q * PERatio
else:
price_target = 0
price_buyin = monthprice.get_PriceHigh(stockid, year_est + 1, 1)
if price_buyin is not None:
price_target_buyin_diff = (price_target - price_buyin) / price_buyin
else:
price_target_buyin_diff = None
if price_buyin is not None and price_target_buyin_diff is not None and price_target_buyin_diff > expect_return and PERatio < 20:
price_high_hold = get_high_price_hold(stockid, year_est + 1, 1,
hold_year)
price_target_high_diff = (price_high_hold -
price_target) / price_target
return_max = (price_high_hold - price_buyin) / price_buyin
else:
price_buyin = None
price_high_hold = None
price_target_high_diff = None
return_max = None
return {stockid: price_target}, \
{stockid: PERatio}, \
{stockid: price_buyin}, \
{stockid: price_target_buyin_diff}, \
{stockid: price_high_hold}, \
{stockid: price_target_high_diff}, \
{stockid: return_max}
def main():
findbillion_database = class_findbillion_database(dataset_path)
for year in year_list:
print('----------------------------')
print(year)
# eps ground truth
financial_ratio = class_financial_ratio(findbillion_database)
eps_true = financial_ratio.get_eps_last_4q(stockid, year, quarter)
# estimated eps
est_eps = class_est_eps(findbillion_database)
eps_pred = est_eps.est_last_4q_eps_by_netincome_ratio(
stockid, year, quarter)
# error
err_percentage = (eps_pred - eps_true) / eps_true * 100
print("eps (true): {:2.2f}".format(eps_true))
print("eps (predict): {:2.2f}".format(eps_pred))
print("error of predict: {:2.2f}%".format(err_percentage))
